Preliminary Conference Program

Autonomous Vehicle Test & Development Conference

Achieving safe autonomous driving through test and development, collaboration, harmonization and standards

HEADSTART project: Harmonized European Solutions for the Testing of Automated Road Transport

Álvaro Arrúe
Project manager connected and automated driving
HEADSTART is an H2020 EU-funded project that aims to define testing and validation procedures of connected and automated driving functions including key technologies such as communications, cybersecurity and positioning. The tests will be in the simulation and real-world fields to validate safety and security performance according to the key users’ needs. The expected impact of the HEADSTART project is based on three main action pillars: testing and validation – potentiation of development strategies bringing time and costs reduction; assessment – creation of assessment protocols increasing vehicle safety awareness; certification – support of regulations ensuring the safe introduction of CAD technologies to the market.

Safety assessment of ADAS using simulation with reliability analysis methods

Maximilian Rasch
PhD and engineer
Mercedes-Benz AG

New approaches for autonomous vehicles certification: a European perspective

Maria-Cristina Galassi
Scientific project officer
European Commission
The presentation will address AV safety verification through audit and assessment of the documentation provided by manufacturers (including simulations), physical testing (both on-track and on-road) and operational feedback from real-life experience.

Validating ADS toward an industrial scenarios database

Marc Pajon
Expert leader - testing and measurement technologies
Renault Group
Validating AV safety is a crucial part of ongoing research. The approach of separately track-testing sensors and driving algorithms is hardly sufficient to demonstrate AV safety. Scenario-based simulation approaches are necessary complements to the traditional approach, allowing computation of a controlled diversity of key variables in many iterations in a safe, fast and documented way. French car manufacturers Renault and PSA, together with academic researchers (VEDECOM, SystemX, Lab and Ceesar) and other partners (Valeo, AVS and Expleo), propose to address the challenge of demonstrating AV safety by taking an array of 'in-the-field' situations into account.

ASAM simulation standards – past, present and future

Benjamin Engel
Global technology manager
In 2018 ASAM acquired its first simulation standards in the form of the OpenX portfolio (openDRIVE, openCRG and openSCENARIO), with the addition of the Open Simulation Interface in 2019. Since then, simulation experts worldwide have been working hard on the first official ASAM revisions, the first of which are to be released in Q1 of 2020. This presentation will give an overview of the ASAM activities to date, and provide some insight into where we see the road leading in 2020 and beyond. Our goal is standardization to facilitate the development of safe, regulated autonomous driving.

SOTIF in a Volvo cars context

Carina Björnsson
Technical expert, driver assistance and active safety test methods
Volvo Car Corporation

Homologation of automated driving functions: worldwide overview, customer acceptance and strategic aspects

Christian Gnandt
Vice president automated driving
TÜV Süd Auto Service GmbH
Homologation of automated driving functions presents a huge challenge for their market introduction. Existing regulatory safety frameworks applicable to conventional vehicles and their components are insufficient to fully assess the operational characteristics of current and future automated vehicle technologies. With increasing automation, vehicles transform into cyber-physical systems that no longer require a human driver; therefore, new safety challenges will have to be considered. This presentation discusses those challenges, provides an overview of the current regulatory and standardization work in progress and explains the possibilities for how to approve automated vehicles for public roads today.

From absolute safety to informed safety: the role of operational design domain

Dr Siddartha Khastgir
Head of verification and validation, intelligent vehicles
WMG, University of Warwick, UK
To prove that automated driving systems (ADS) are safer than human drivers, it is suggested that they need to be driven for over 11 billion miles. A number of miles is not an appropriate metric and doesn’t guarantee absolute safety. This highlights the question: “How safe is safe enough?” To answer, we suggest a departure from the world of absolute safety to informed safety. A key aspect of informed safety includes an accurate and standardized definition of the operational design domain for an ADS, including conveying it to users, regulators and other stakeholders. The first step in all safety standards: ODD definition.

Coordination of R&I and pilot activities in Europe: how can we learn in order to scale up?

Stephane Dreher
Senior manager - connectivity and automation, blockchain
Ertico - ITS Europe
Many research and innovation, testing and piloting activities are being carried out independently across Europe either through EU-funded consortia projects or at the national level driven by Member States, industry or public-private partnerships. The lack of alignment and common approaches often results in duplications or overlaps, prevents comparability of results and the development of interoperable solutions, and hampers the harmonized deployment of CAD in Europe. In an effort to facilitate the exchange of lessons learned and best practices, as well as the identification of synergies and gaps between projects, the EU-funded ARCADE Coordination and Support Action has set up a comprehensive knowledge base on CAD-related activities in Europe and beyond, which is currently being populated in collaboration with the EU CCAM Single Platform. The project is also organizing concertation events to support consensus-building across stakeholders on methodologies or the identification of next R&I actions on CAD. This presentation will provide an overview of the CAD knowledge base and key outcomes from other harmonization-related activities of the project on methodologies or description of use cases.

Humanizing autonomy: setting a global standard for how autonomous systems interact with people

Raunaq Bose
Co-founder and CTO
Humanising Autonomy
By building a human intent prediction software, we are able to increase VRU safety while also improving public acceptance of AVs.

Better safe than sorry – safety assessment for self-driving vehicles

Mario Torres
This presentation outlines a method for defining safety measures and provides concrete examples of such measures. Safety measures are a vital element for validation of the behavior of highly automated vehicles – that is, for the assessment of the behavior of such vehicles from an observer's point of view. Validation complements the verification process, which studies the correct functioning of a system and its components compared with their requirements.

Assurance cases for automated driving

Rasmus Adler
Program manager
Fraunhofer IESE
This presentation covers challenges, solutions and standards for arguing the safety of automated driving. In the V&V Methoden project, Fraunhofer IESE is working on a safety argument that can be based on evidence from testing and simulation. The talk will summarize the current results of this project. It will also discuss UL 4600 and the upcoming application guide of the AK DKE/AK 801.0.8 Spezifikation und Entwurf autonomer/kognitiver Systeme (specification and design of autonomous/cognitive systems).

Validation and verification methodology for AI in autonomous vehicles

Leon Altarac
VP business development
The appearance of autonomous vehicles (AV) on public roads is promised to be a reality soon, but some critical aspects of this reality are yet to be resolved. One of these is the lack of an efficient safety performance verification technique, as the existing tools for hardware and software reliability and safety engineering do not provide a comprehensive solution regarding algorithms that are based on machine learning (AI). To start tackling this problem, a methodology based on statistical testing in a simulated environment is presented and demonstrated on full-scale autonomous vehicles.

Mapping, positioning and connectivity

Bridging the gap between AV navigation software and hardware

Greg Drew
Polysync Technologies
Currently there is a gap between autonomous vehicle (AV) navigation software and physical operation. This presentation will discuss the current status, challenges, pitfalls and best practices for integrating AI and physical navigation systems and ensuring autonomous vehicle safety. The business results of achieving the ability to write once, run anywhere include efficiency, safety, fastest time-to-market, normalization and best-practice optimization. A universal safety standard for integrating software and hardware navigation systems will streamline and normalize the development and optimization of AV navigation systems, accelerate market entry and achieve the promise of AVs to dramatically improve safety.

The collaborative way forward: open data for an autonomous future

Emil Dautovic
Vice president automotive
For autonomous vehicles to safely navigate our roads, they need meticulously detailed and accurate information about the world around them. No single actor is capable of collecting all the data necessary for these vehicles to operate in all locations and situations, so the only way forward is to open and share data. This collaborative approach has driven the growth of roughly one billion street-level images on the Mapillary platform – all used to create data sets for training machines to see and understand the world, and to build and maintain the high-definition maps required for autonomous vehicles to be deployed globally.

The challenges in testing connected vehicles

Stoyan Nikolov
Test analyst
McLaren Applied Technologies
The complexity of connected and autonomous vehicles increases significantly with the introduction of multiple connectivity channels and sensors. The requirements for robust and uninterruptible connection provoke the need for multiple modems fixed to multiple network operators, being able to switch among various wireless networks (such as 3G, 4G and 5G). The introduction of geofence-triggered software features requires GPS/GNSS connectivity in addition to the mobile network. Testing the connectivity is a challenging task that requires realistic simulation of the mobile network conditions and GPS/GNSS. This presentation will cover the challenges of testing the connectivity channels for connected vehicles, the simulation scenarios to be considered and the challenges of simulating a fleet.

On-track testing of connected vehicles: methodology, challenges and results

Annie Saleh
Head of automated and connected driving
PMG Technologies
PMG Technologies has completed closed-circuit track testing for Transport Canada to evaluate the performance and effectiveness of crash avoidance technologies, specifically connected vehicle technology. Tests were performed using two DSRC-equipped vehicles to reproduce maneuvers that would trigger targeted V2V features (hard braking, slippery road and disabled vehicle). The test results are used to describe and quantify the timing of visual and audible alerts sent to the driver after the reception of basic safety messages. This presentation discusses the testing methodology and challenges of physical testing on tracks. It highlights the analysis of test results and demonstrates the importance of track testing.

Pedestrian protection through automated driving

Hartmut Runge
Project manager DriveMark
A new concept for an autonomous driving system is presented, which in particular protects other road users such as pedestrians and cyclists. Furthermore, the system automatically ensures compliance with traffic regulations in a smart city equipped with it. It goes far beyond what we have seen so far with lane-keeping or automatic braking systems. The concept of geofencing will be further developed with unprecedented granularity. Detailed maps of the traffic areas of a city with 4in resolution will be used.

Automated public road testing based on digital twins

Patrick Luley
R&D manager - automated driving
Joanneum Research - Digital
To pave the ground for salable and cost-efficient test and validation of AD functions by real testing on public roads, Joanneum Research is producing Ultra High Definition Maps (UHDmaps) based on mobile mapping data in a salable automated workflow. UHDmaps contain a digital copy of reality, which sets the benchmark for the digital assessment of automated driving functions. The depicted solution is already utilized by the Austrian Light Vehicle Proving Region for Automated Driving (ALP.Lab GmbH) and its partners. The presentation will give an overview of the technical solution and certain test use cases.

Precise HD map data as the basis for virtual testing and simulation

Dr Gunnar Gräfe
3D Mapping Solutions GmbH
Artificially designed digital roads may help case by case, but for various applications the precise digitization of real-world roads is needed. The typical requirement is that the roads used for virtual testing and simulation are regarded as identical digital twins, which is a prerequisite for comparable testing in reality and virtual environments. 3DMS has invented the necessary technology for more than 20 years and generates high-resolution digital road-surface models in OpenCRG format, or produces precise high-definition reference maps in OpenDRIVE format, which are either used for virtual simulation and testing or as reference maps in the car for autonomous driving development. The presentation will show various project examples.

Managing the test and development process – best practices for accelerating development and achieving safe autonomy

Building a toolchain and an ecosystem – the strategy behind creating a new company for autonomous solutions

Magnus Liljeqvist
Global technology manager - infrastructure
Volvo Autonomous Solutions

Scaling a simulation toolchain for higher levels of autonomy

Dr Sandeep Sovani
Global director, automotive industry
Ansys Inc
Driving automation software for ADAS Levels 1-2 is now routinely validated by software-in-the-loop (SIL) simulations. Moving up to Level 3 greatly increases the complexity of the automated driving system, thus increasing validation needs exponentially. This talk presents the ANSYS Autonomy toolchain for virtual validation and sign-off of automated driving features at Levels 3-4. For these levels, beyond SIL simulation the toolchain includes software solutions for scenario collection via drive data analytics, scenario curation, scenario variation, test plan management, robustness testing of perception software, simulation result analytics for coverage analysis and building statistical validation cases, simulation data management and toolchain validation.

Product management for autonomous vehicles

Todd Medema
Product manager
Uber ATG
Most product management resources are focused on B2C or even B2B. But what does it take to design and build internal tooling to accelerate autonomy development? In this talk, we'll cover some of the unique challenges in products built by engineers for engineers, capable of handling immense datasets and complexity, in an unsolved and ever-changing solution set.

Ensuring successful implementation and uptake of new mobility services

Kevin Vincent
Director - Centre for Connected and Autonomous Automotive Research
Coventry University
The technological challenges/opportunities of CAV/CAM and the roadmap to autonomy are maturing. However, the requirements to generate market pull and the skills required for the successful implementation and uptake of services are less well understood. For example, safe ongoing operation of vehicles needs new MOT tests addressing security, software and data privacy; new and disruptive ownership models present issues and opportunities regarding individual design and brand identity; trust and perception require more human-centered design for viable solutions. These and more questions need answering if economic, environmental and productivity projections are to be realized.

Simulation, scenarios, verification and validation

Large-scale simulation supporting HARA for ISO 26262, SOTIF and SPTA

Dr Edward Schwalb
Lead data scientist
MSC Software
We will review a Bayesian approach to hazard analysis and risk assessment, which gives rise to ‘smart hazardous miles’. We describe a generic hazard control loop that enables active avoidance of accidents, and illustrate how it can be applied to classify hazards as potential or developing, consistent with the UK theoretical driving test administered to human drivers. This approach further guides our development of tools enabling accelerated development and testing. We discuss the implied hazard specification extensions to the PEGASUS approach. An integrated workflow will be presented, which is consistent with ISO 26262, SOTIF and STPA.

Obstacles on the road to massive scenario-based verification

Yoav Hollander
Founder and CTO
Foretellix Ltd
There is now a growing consensus that massive scenario-based testing should be a major component of verifying ADAS/AV safety. However, experience shows that when one goes from, say, 1K scenario runs to 10M scenario runs (per week), some serious new problems arise. For instance: understanding what was already tested (and where the 'holes' are) can be challenging; lack of repeatability can make debugging a big challenge; checking the results (and handling 'gray areas') can become a nightmare.

Enabling virtual test and validation: creating a virtual proof of validation in the ENVITED ecosystem

Carlo van Driesten
Systems architect for virtual test and validation
BMW Group
New forms of cooperation are necessary to turn the vision of autonomous driving and fully connected mobility systems into a reality. Virtual validation is an essential part of the development process. Standards for model and system interchange are vital for cross-company and cross-domain virtual integration and simulation of HAD functions. Standards like FMI, SSP and the OpenX at ASAM eV showcase current possibilities, challenges and future directions, as well as a vision of future collaborations. The foundations for the future ecosystem have been laid by the ENVITED ecosystem: standardized data for virtual test and validation; open and modular simulation architecture; traceability of standardized data and test results for a virtual proof of validation.

Enabling scenario-based verification for autonomous driving

Alexandre Mugnai
Business development manager
Siddhant Gupta
Research engineer, verification and validation, autonomous driving/ADAS
Volvo Cars Corporation
This paper proposes a methodology to enable scenario-based verification for autonomous driving by creating a scenario and test database using a suitable design of experiments. Consequently, an optimization strategy is developed to evaluate the criticality of the aforementioned test cases to segregate the test cases to be simulated in a software-in-the-loop (SIL) environment and the critical test cases in the vehicle-in-the-loop (VIL) environment to ensure the coverage using SIL and VIL platforms. The toolchain to create the design of experiments and optimization strategy is the Simulation Platform Active Safety (SPAS) virtual platform enabling SIL verification developed at Volvo Cars, which is co-simulated with the multi-domain optimization tool modeFrontier.

Panthera driving simulation framework with high-quality content for human-in-the-loop testing

Jelle van Doornik
Product manager ADAS and AD
Although the industry seems to be focused on full autonomous driving (L5), Cruden believes that partial automation (L1-L4) is going to be around for decades to come. The increasing number of automated driving functions causes the interaction between driver and vehicle to change. The most time-effective way to test and validate this properly is with an open-architecture simulation framework that allows easy integration of the customer's engineering tools while displaying high-quality content for the human driver. The Panthera driving simulation framework provides a safe and cost-efficient way to add human-in-the-loop simulation to the existing development, test and validation toolchain.

OmniCAV: hybrid simulation for AV stack verification

Dr Gavin Jackman
Managing director
OmniCAV is a consortium project that is partly funded by the UK Government. It aims to deliver a highly realistic simulation environment for AV stack verification that considers all road users and road types. A unique environment that covers all road types and eventualities is being created and validated with real AV testing within the OmniCAV project. Our consortium is applying cutting-edge technology in creating this environment, with high-fidelity lidar mapping, mixed-traffic simulated environments with motorized and non-motorized vehicles (bicycles) and pedestrians, advanced driving simulators and more. The solution will cover the urban environment, the strategic road network and the rural road network, allowing all conditions to be simulated and evaluated. This is being delivered in combination with a UK local government and a Zenzic/CCAV-funded testbed location.

Edge-case hunting in scenario-based virtual validation of AVs

Dr Henning Lategahn
Atlatec GmbH
Validating ADAS and AV stacks in large parts in virtual environments using simulators is a given nowadays. The industry largely agrees on a quality over mere quantity view on things. But what is the quality of a scenario set that is tested against? It essentially boils down to the ability to identify edge cases that show the exact limits of the system under test. In this talk, we present how one can use real-world scenarios, translate these into their digital-twin counterparts for simulation and finally modify these to identify the crucial edge cases.

Autonomous vehicle engineering simulation tools for appropriate driver acceptance and comfort

Dr Andras Kemeny
Expert leader immersive simulation
The challenge of autonomous vehicle validation relies on massive simulation, due to the vast number of kilometers to run in various road, traffic and weather conditions. Nevertheless, acceptance of the proposed automation system will have a heavy impact on the use of the system and the efficiency of handling it when sharing or taking back vehicle control. Driver-in-the-loop simulation will play an essential role, using high-performance driving simulators or dedicated configurations, including virtual reality and web-based online solutions. These simulation tools will be essential to complete efficient autonomous vehicle engineering design for driver acceptance and comfort.

Developing a future-proof scenario database in a world of emergent standards

Mike Freeman
Project engineer
Warwick Manufacturing Group
Testing is fundamental to the safety of automated driving software, but driving billions of miles to achieve sufficient scenario coverage is unfeasible and requires a better approach. Scenario sharing across the industry is gaining support as being the solution. With this aim, the standardization of scenario description is being worked on but we are still some way from a universal standard. This puts the system architect in a difficult position: how to design a scenario database that will support today’s standards as well as those of tomorrow? As part of the UK’s Midlands Future Mobility project, we answer this question.

Generation of safety-critical scenarios for validation of autonomous vehicles

Julien Niol
Research engineer
Autonomous vehicles are introducing a new challenge to secure them by relying on algorithms to analyze their environment and take decisions accordingly without any human supervision. This paradigm requires the consideration of not only functional safety but also the safety of the intended functionality (SOTIF). We present how we have experimented with a model-based safety assessment methodology to generate classes of safety-critical scenarios for the validation of autonomous vehicles. This approach is based on a high abstraction level behavioral model integrating the system architecture and its immediate environment, developed to address key concepts introduced in ISO 21448.

Automated validation toolchain for autonomous driving functions

Thorsten Drogge
System architect
Elektrobit Automotive GmbH
Handling huge amounts of raw sensor and vehicle bus data in the hundreds of petabyte range, as well as maintaining a precise and comprehensive sensor model tightly coupled to a restbus simulation, poses a major challenge for sufficiently testing and validating automated and autonomous driving functions. Providing a high-performance web portal to ingest, track and reprocess such data in the cloud or from locally attached mass storage of hardware- or software-in-the-loop simulation solutions and driving scene catalogs enables the task of data orchestration to be mastered. In addition, suitable tooling provides hardware support for maintaining a precise sensor model and bus simulation to emulate a full automotive restbus to the device under test.

Vision, lidar and sensor test and development

New consumer-friendly ADAS rating system including innovative lidar solutions

Dr Mircea Gradu
Senior vice president quality and validation
Velodyne Lidar
Rapid developments in the field of driver assistance technologies require clear communication from the industry. Customers need crystal-clear information about what their vehicles can do. Simply naming ADAS functions such as LKA or ACC as a feature is not enough. A clear, general nomenclature is needed to understand and accept new functions. Service descriptions must be refined, and test protocols must be adapted to the use cases. More security through new sensor technologies requires common, transparent standards that must be clearly communicated in order to enable responsible implementation. The presentation gives an overview of identified shortcomings in the current ADAS sensor taxonomy, testing and validation and proposes a standardization approach leading to a new ADAS feature rating system.

Synthetic data utilization for AD/ADAS sensing functions performance evaluation – challenges and opportunities

Farid Kondori
Verification tools lead
To tackle the problem of verification and validation of autonomous vehicles, synthetic data generated in virtual environments can be employed to complement field testing, due to the fact that these environments are highly scalable and inexpensive. Although in recent years virtual environments have been employed by the community to develop and test ADAS/AD functionalities, such as AEB or ACC, there has been limited possibility to utilize synthetic data for sensing functions performance evaluation. This presentation will address the challenges and opportunities of virtual environments for test and validation of camera-based sensing algorithms.

Modular solid-state lidar to find the best fit for automotive applications and car integration

Filip Geuens
XenomatiX – True Solid State Lidar
XenomatiX and Marelli have elaborated a modular solid-state lidar approach. Based on compact modules, different lidar systems can be composed to find the right fit between lidar performance, application requirements and vehicle integration restrictions. A number of system configurations will be explained and detection capabilities will be shown. As Level 3 ADAS is the next target for automotive mass-production vehicles, a high-resolution front-view solid-state lidar configuration will be presented.

LWIR thermal sensing is a must in the autonomous suite

Raz Peleg
Sales director
The current trio sensing suite of CMOS, radar and lidar fails to address certain corner cases. Adding LWIR thermal sensing to this suite will allow all-weather detection and classification, VRU classification even through fences, VRU classification when only partial body is exposed (between parked vehicles), a solution for blinding situations and faster AV shuttles. The sensor is passive and cost-effective and can support the shift from prototypes to mass autonomous production.

See the unseen: the future of driving

Tim LeBeau
Chief business development officer
Seek Thermal
The best vehicles on the market are hyper-aware and able to detect challenges in their path more quickly than occupants. As autonomous vehicles begin to hit the road, it is imperative that ADAS are optimized to safely avoid hazards with speed and accuracy. The presentation will explore concerns around autonomous vehicles and share the impact of thermal sensors on minimizing human control in driving with real-time insights. It will discuss how data collected from thermal sensors can be leveraged to identify impending dangers before they occur, sharing how temperature values create visual images, accurately distinguishing living from non-living objects.

Physics-based sensor simulation – essential for developing the safest autonomous vehicles

Serge Laverdure
Connected and automated unit director
The certification of autonomous vehicles will require more and more virtual testing as a consequence of a new level of complexity that the automotive industry has never faced before. Nevertheless, real testing should evolve accordingly and should be supported by interoperable software tools. ESI is providing an interoperable solution relying on high-fidelity physics-based sensor models to tackle the harsh weather conditions in which autonomous vehicles will operate in the field (fog, rain, snow, dust and sand). The aim of this presentation is to evaluate the effect of harsh weather conditions on lidar performance.

Verifying L2+ collaborative driving and ADAS functions using DIL

Ram Mirwani
Director, global business development, ADAS
Konrad Technologies & VI-grade
As more standards are worked on and released for defining autonomous driving capability, the test focus on L2+ ADAS features being released in several vehicles by OEMs continues to intensify. With collaborative driving taking center stage for near-term autonomous driving functions, key variables to verify for L2+ ADAS functions include driver reaction time, acceptable tolerances for taking over control and overall user experience. In this session, VI-grade and Konrad Technologies will highlight how DIL with sensor fusion test is a platform for jointly verifying the functional performance and driver experience for L2+ ADAS functions.

Real-time over-the-air automotive radar hardware in the loop to test autonomous vehicles

Dr Kasra Haghighi
Fahimeh Rafieinia
Autonomous driving will revolutionize the future of mobility. The main pillar of autonomous driving, as well as advanced driver assistance safety systems (ADAS), is sensing. Among all sensors, radars are the most reliable and versatile ones, providing environment perception. Test and validation of radars and radar-based safety functions is a necessity that requires an advanced OTA radar HIL setup where rich and dynamic traffic scenarios can be emulated. Certain real-time requirements need to be established between environment simulator and radar-target-simulator. This presentation will demonstrate a full OTA HIL setup enabling emulation of more than 200 moving targets for the radar under test.

Platform-based approach for a future-proof ADAS V&V infrastructure

Balazs Toth
Technical sales project manager
National Instruments
The growing variety, resolution and data rate of modern ADAS sensors and components require adequate validation and verification systems and methods for the validation and verification of ADAS sensors and ADAS sensor fusion systems. The use of an open, scalable, modular, high-performance ADAS record-and-replay platform provides an optimal way for addressing the current complex ADAS V&V challenges.

Reference measurement system for development and validation of ADAS functions

Steffen Metzner
Technology scout ADAS, simulation and control
AVL List GmbH
Despite all efforts to carry out tests of vehicle automation functions in simulation, many tests still need to be executed directly in-vehicle. For efficient development and verification of sensor systems, a highly accurate reference sensor system is necessary. Without such systems, troubleshooting and objective evaluation of vehicle behavior becomes very expensive. The solution discussed in this presentation aims at supporting the development of vehicle automation functionalities during the entire development process from sensor selection to function validation. The main advantage is the possibility to get feedback in the vehicle, which can be refined in post-processing.


Dr Simon Funke
Partnership Manager Automotive
understandAI GmbH
The testing of the autonomous driving function requires a huge number of miles to be driven to secure a safe operation. To track development, miles-per-disengagement are reported. Lastly, alternative metrics have been proposed, such as the Driverless Readiness Score that also takes false negative and simulation into account. We propose a data setting, where raw lidar data is translated into simulation models where the data is augmented by varying the environment, the traffic and especially the occurrence of critical situations. Through sensor realistic simulation, different sensors and sensor positions can be analyzed based on one single data set, thus reducing the number of miles to be driven drastically. We prove our approach by showing a better perception performance on the augmented data set compared to the raw data.

Solid-state lidar based on liquid crystal metasurfaces

Gleb Akselrod
Since the first mechanical lidar sensors surfaced for use in robotic cars, multiple approaches have been proposed to address the challenging performance requirements in terms of range, field of view, resolution and frame rate. Although OPA- and MEMS-based systems show some promise in cost-effective design, the small aperture of the mirrors or phased arrays in these systems makes it impossible to meet industry performance goals. A lidar based on liquid crystal metasurfaces (LCMs) manufactured in CMOS silicon can be built with large optical aperture size, yielding good optical performance while also achieving low cost in mass production.

Best Practices & Innovation in Test & Development

How augmented reality and vehicle-to-pedestrian communication increase safety

Robert Kempf
Vice president sales and business development ADAS/autonomous driving
Harman International
Vehicle-to-pedestrian (V2P) communication is an innovative application of ADAS technologies that has the potential to significantly increase public safety. V2P proactively alerts automobile drivers and pedestrians to possible safety conflicts through the use of vehicle-to-everything (V2X) technology. In the car, V2P can work on low-latency 5G peer-to-peer signals to identify objects in the vehicle’s path through proximity scanning. Similarly, pedestrians or cyclists with a C-V2X-enabled mobile device will also receive an alert that a vehicle is entering their path. With this improved awareness, vehicles, pedestrians and cyclists can be alerted to potential safety conflicts even in situations where advanced cameras can’t see past physical obstructions – such as around corners or through parked vehicles. This presentation will give an overview of the status of the technology, current developments and V2P's potential for pedestrian safety as well as the industry.

Latest achievements in the world of connected driving

Thomas Jäger
Senior vice president and head of technology technical service management
Dekra SE
The presentation will discuss the latest V2X technology developments (ITS-G5, DSRC-wave, cellular); the latest regulatory and certification situation (EU, USA and others); testing requirements for connected and automated driving; the most recent developments in regional and global interest groups; challenges and outlook for the future.

Data management, storage and cloud technologies

The road to cloud-based validation for autonomous vehicles

Janek Jochheim
Product manager cloud and SaaS
It is common knowledge in the automotive industry that simulation and software-in-the-loop are key elements for the validation of autonomous vehicles. These simulations will usually be executed in the cloud. But how can a user get to a scalable cloud simulation and – even more importantly – how can they choose and execute the right test cases? And is this 'just' a technical challenge? This presentation discusses the different topics that have to be addressed in order to get to simulation and validation in the cloud, and points out the major challenges on the way.

Have we collected enough traffic data?

Sytze Kalisvaart
Project manager StreetWise, integrated vehicle safety department
Many OEMs and other automated driving companies are collecting massive amounts of driving data to identify what scenarios the automated vehicle might have to deal with. Through scenario extraction, repeated driving patterns are categorized and turned into statistics essential for effective safety assessment. But when is the data collection enough? The TNO StreetWise scenario database includes completeness indicators at various steps in the scenario mining pipeline. We will introduce the meaning and application of these completeness indicators. In this way, OEMs can compare coverage of their data collection and quantify the completeness of the collected data.

Introducing data center technologies to simplify data harvesting in test drives

Johannes Zangerle
Technical business developer
b-plus GmbH
Sensor bandwidths and the complexity of automated vehicle setups are the main challenges for harvesting data during test drives. Measurement tasks are moving toward multi-gigabit data rates with many raw data and metadata streams. Intelligent data distribution and time correlation are crucial for exact data acquisition. In the end, data integrity and time correlation are key for further analysis and AI lessons. This presentation shows how data center technologies such as RDMA over Converged Ethernet and Ethernet switching fulfill the requirements of Level 4 or Level 5 systems, and how an intelligent data recording infrastructure reduces data management complexity.

Validation in the virtual domain

From ideal to full physics: why XIL simulation for developing and validating AD/ADAS requires multi-level sensor models

Thomas Nguyen That
Head of automotive domain
AV Simulation

Vehicle-in-the-loop – bridging the gap between simulation and real world

Dr Tobias Düser
Department manager
The integration and validation of ADAS and AD functions require new approaches in the vehicle development process. AVL’s vehicle-in-the-loop approach combines a vehicle testbed for the complete and integrated vehicle with a detailed simulation environment. Different components such as a dynamic steering force emulator, sensor over-the-air simulators, etc empower the vehicle testbed to be suitable for ADAS and AD validation. As part of an overall virtual testing toolchain, this approach helps to deal with the test coverage while reducing test effort. All scenarios can be performed at vehicle level under highly reproducible and safe conditions. Different application examples will be introduced.

From real driving data to concrete test scenarios

Florian Hauer
Chair of software and systems engineering (department of informatics)
Technical University of Munich / ITK Engineering GmbH
We present a holistic approach that takes recorded traffic scenario instances and yields 'good' test scenarios for automated and autonomous driving systems. Such test scenarios are usually generated from scenario types, for which we present an approach that allows measuring both the test case quality and system behavior. Since this requires completeness of the list of scenario types, we provide both a statistical model and a methodological approach to assess completeness. To achieve the latter, we automatically derive scenario types from real data, which complements current manual scenario derivation. We show technical solutions for each of the steps presented.

AD-EYE: a simulation platform for automated driving systems

José Manuel Gaspar Sánchez
Research engineer
KTH Royal Institute of Technology
Automated driving systems (ADS) require solving a multitude of capabilities including perception, decision making and planning in real time. Each of them represents a challenge on its own, and researchers usually focus on one while abstracting away the rest of the system. In this presentation, we will introduce the AD-EYE platform, which has been developed under several EU projects and with the collaboration of multiple industrial partners. The goal of the project is to provide a simulation platform for ADS-related simulations with common base functionality that can be modified as per need. The aim is to provide better integration of the different projects by letting research groups work in a common environment. AD-EYE targets, in particular, dependability-related evaluation of architectures and algorithms for highly automated vehicles; as a prominent feature, it provides a configurable safety supervisor architecture. The talk will present the current status of the platform, illustrate its use and discuss its current roadmap.

Validation and performance evaluation tools for higher-level autonomous perception

Michel Berendes
Project manager ibeo.Reference
Ibeo Automotive Systems GmbH
Development and validation of ADAS/AD sensor perception require solid ground truth to be successful. With projects aiming for autonomous functions Level 3 or higher, manual ground truth labeling has reached its limitations in acceptable cost and time consumption. ibeo.Reference offers a chain of highly automated tools to enable ADAS and AD function development companies to provide their teams with the fast and reliable ground truth their projects require. This presentation will offer an overview from recording a parallel perception of the vehicle’s environment, through automatic and smart-manual ground truth generation, to automated comparison and perception performance calculation.

Real-world and open-road test and development

Practical implications of steward-less autonomous vehicle testing and operation

Tom Jansen
Global domain leader connected and automated vehicles
Ricardo plc
We are seeing the deployment of many 'novel' pilots with self-driving vehicles around the world. Looking more closely, we see that often these vehicles feature a steward or safety driver on board, who is legally in control of the vehicle at all times. With new legislation slowly allowing testing without stewards on board, it is essential that we understand the practical implications for autonomous vehicles operating without safety drivers. In this session we will explain the implications for CAV design and testing from our practical experience working with industry leaders in recent (truly driverless) CAV projects.

Level 4 AV testing for urban environments: challenges and opportunities

Mohamed Azhar Halikul Zaman
Research engineer
As autonomous vehicles (AVs) increase in maturity, the complexity in ensuring they are safe increases as well. The traditional automotive testing methodologies need to evolve to suit the ever-changing nature of AVs. This will bridge the gap between regulators and AV developers, and eventually lead to safe and effective implementation of AVs. In this presentation, CETRAN will present the key challenges it faces when testing Level 4 AVs, and will share its approach and the ongoing research/projects to tackle these challenges. The focus will be on the current unresolved issues in virtual and physical testing.

Robopilot – Level 4 autonomous driving on mixed roads

Nicholas Clay
Head of homologation and quality
The presentation will outline the challenges, lessons and successes of Robopilot – a UK CCAV-funded project delivering a demonstration of Level 4 autonomous driving on mixed roads in the UK. It will focus on the testing and validation journey from research and simulation to on-road testing and live demos. Robopilot is a £12m consortium project based in the UK. Partners include UPS, Thales, Bristol Robotics Lab, Loughborough University, TVS and South Gloucestershire Council.

Applying the PEGASUS approach to automation for the urban environment

Dr Hardi Hungar
Team Leader Verification and Validation Methods
German Aerospace Center
The PEGASUS project developed and demonstrated a method for the validation of automated driving functions for the highway domain. Two projects currently elaborate on this approach and apply it to the far more complex urban environment. Simulation is supposed to provide the bulk of evidence for the homologation of the vehicles. For that, the simulation must be adaptable to various tasks in the verification and validation chain. And, of course, the simulation results must be validated. One project, SET Level 4-5, is developing simulation technology based on a modular architecture with standardized interfaces. The other, VVMethoden, covers the full development lifecycle and employs simulation technology. The talk will present the intended role of the simulation and the projects' approach to providing the technology with the desired features.

Deploying autonomous buses in mixed traffic

Jorgen Kjaer
Business development manager - autonomous buses
Vy Group
Vy Group is a leading bus operator in the Nordics, operating over 3,000 buses. We are taking a leading role in the deployment of autonomous buses in public traffic. In a successful project in Kongsberg (Norway), two autonomous buses have replaced a regular diesel bus in operation (mixed traffic). This winter, we will test driving without a safety host on some parts of the route in Kongsberg. We will also test operation without a safety driver in an industry park, with traffic lights. We also have a project for bigger AV buses.

CAV testing on public roads – crucial learning or unnecessary risk?

John Fox
Program director – Midlands Future Mobility
WMG - University of Warwick
Testing of CAVs on public roads is a hot topic. Does it expose the public to unnecessary risk, or is it essential for profound safety improvement on the world’s roads? Can we have the best of both worlds: on-road learning with enhanced safety? The presentation addresses these questions, using the £35m Midlands Future Mobility test and trialling ecosystem as a case study. There are exciting times ahead!

Automated driving grows out of the niche

Dr Eric Sax
Head of Institute of Information Processing Technology
Karlsruher Institute of Technology (KIT)
The motivation for the introduction of autonomous driving differs between road users. For passenger cars, safety and comfort are driving forces. For commercial vehicles, economic reasons are most promising. Increasing the service times of trucks and buses by supporting drivers or even substituting them with advanced driver assistance systems is a huge business case. The idea is to start in areas that are closed to ordinary traffic: on depots. This application domain is followed by bus rapid transport in special lanes, highways and situations that promise a controllable environment. A stepwise approach is most promising and the innovation will be derived from the niche. The idea is to learn and experiment there and, step by step, enhance the field of use.

Transitions for Level 3 automation

Anne Klamroth
Research fellow
Bundesanstalt für Strassenwesen (BASt)
Level 3 automated vehicles require that the driver takes over control if system limits are reached. But can traffic conditions have an influence on the takeover? To investigate this question, the Federal Highway Research Institute conducted a Wizard-of-Oz study on federal motorways in the Cologne-Bonn area. Thirty-nine test participants were involved in the study; this talk will present the findings and potential consequences.

Autonomous Vehicle AI, Software and Sensor Fusion Conference


Bridging ADAS to AD in mass production

Dr Duong-Van Nguyen
ADAS department manager
Panasonic Automotive Europe
The presentation will discuss the gap between current ADAS and expected ADAS and AD systems. It will also outline technologies to enable 3D sensing using low-cost sensors by available ADAS ECU, and examine advanced sensor fusion to compensate for the deficiencies of one by the abilities of others.


Automotive Grade Linux: enabling industry collaboration through open-source software

Dan Cauchy
Executive director, Automotive Grade Linux
The Linux Foundation
The Automotive Grade Linux (AGL) community consists of more than 150 companies across the automotive and tech industries who are working together to develop an open-source software platform for all in-vehicle applications from infotainment to autonomous driving. Sharing a single software platform across the industry decreases development times so OEMs and suppliers can focus on rapid innovation and bringing products to market faster. This talk provides an overview of AGL, production use cases including Toyota and Subaru, the project roadmap and how to get involved.


ADAS/AD virtual end-to-end software development

Dr Clara Marina Martínez
Engineer - ADAS virtual development
Porsche Engineering Services GmbH
ADAS/AD software development needs to cope with complex sensor systems, plentiful corner cases still to be discovered and a cumbersome number of kilometers to test/certify. These tasks require high support from virtualization to be achievable under challenging deadlines and at reasonable cost. The perfect tool that gathers all your requirements does not exist. However, many high-quality software solutions are able to simulate sensors, traffic, vehicle dynamics, driver behavior and realistic environments with the level of detail that every project needs. At Porsche Engineering, we bring together the best tools, data sources and our experience in automotive, to create a flexible simulation platform to support end-to-end ADAS development.


Autonomous driving and open-source technology – does it fit?

Andreas Riexinger
Product manager
Robert Bosch GmbH
Automated driving solutions introduce a new complexity into the development of embedded systems in cars. This complexity rises with each level of control and autonomy. The toolchain for such challenges is also complex and the integration of all the tools requires considerable effort without a real competitive advantage for the automated driving solution. Instead of solving these challenges alone, wasting lots of money along the way, Bosch's automated driving division has started an open-source community known as OpenADx. This talk will present the open-source approach, the current state of the community and the currently available solutions.


Building an ADAS test and development environment in the cloud

Gabriel Sallah
EMEA HPC and big data architect, autonomous driving platform solutions
United Arab Emirates
This session will focus on the key Azure Cloud services needed to meet the demanding end-to-end requirements of testing and validating autonomous driving vehicles: from large-scale data ingestion (PB), to large-scale simulations (60,000+ cores) using high-performance computing (HPC), to scalable machine learning model creation, deployment and management. The presentation will share real-world experience of successfully building this platform for major OEMs and tier suppliers.


Use of artificial intelligence in the validation domain

Ahmed Yousif
Software design engineer
Valeo Schalter und Sensoren GmbH
The presentation will talk about the challenge of annotation and how it is solved using machine learning and AI. It will include a demo related to the topic and also some videos and deployment examples.


The future of driving behavior in autonomous vehicles

Davor Andric
CTO AI and analytics North and Central Europe
DXC Technology
The vision for fully autonomous vehicles has yet to be realized. How realistic is it? Despite the increase in commercially available autonomous features up to SAE Level 3, achieving Level 5 autonomy will require a very different development approach. We will review current approaches and challenges for autonomous driving development, including human driver behavior, and examine what is needed to develop autonomous driving technologies for intelligent and safe real-time driving.


Paving the way for autonomous driving

Bryan Berezdivin
Autonomous systems
Connected, autonomous, shared and electric vehicle trends are converging to revolutionize the automotive industry. In this unprecedented age of innovation, automotive companies rely on Amazon Web Services (AWS) to fuel their digital transformation efforts and get their products to market more quickly, while retaining ownership and control of their data and brand experience. Learn what challenges autonomous driving is posing for developers, and how the main players in the industry are addressing those challenges.


The AV test fleet of the future is virtual

Serkan Arslan
Director of automotive
Nvidia EMEA
Self-driving cars can improve safety while giving people the freedom of mobility. However, validating a self-driving vehicle solely using physical on-road testing is an insurmountable task. As humans, it’s easy to identify situations in which we’re accident prone, but it’s impossible to train for rare, dangerous scenarios/extreme conditions, or at large-enough scale. Through advances in AI and accelerated computing, simulation has emerged as the ideal solution for safely testing and validating AVs. Learn about a unique virtual AV test fleet in the cloud that will enable AV development and validation without putting others on the road in harm’s way.


AI and big data management for autonomous driving (AD)

Frank Kraemer
Systems architect
Advanced driver assistance systems (ADAS) are already becoming part of all vehicles today, and fully autonomous driving (AD) is in the development phase right now. To win this race, applied artificial intelligence (AI) is the key component. All major OEM and Tier 1 auto manufacturers are implementing and testing AD facilities. Developing and testing AD systems requires the storage and analysis of more data now than ever before. Researchers and developers who can deliver insights faster while managing rapid infrastructure growth will be poised to be industry leaders.


ADAS development and validation workflow and methodology

Dr Florian Baumann
CTO automotive and AI
Dell Technologies
IT and AI are key components of your development toward autonomous driving and the next generation of ADAS. Using an efficient workflow, you can make your engineers extremely productive and happy. This session will introduce you to the complete workflow of AI-enabled ADAS/AD product development.


Scale, variability and accuracy: synthetic data sets for training and validation

Danny Atsmon
Founder and CEO
Machine learning and deep neural networks require tremendous quantities of data for training and validation; but even at scale, raw, repetitive or inaccurately labeled data doesn't produce results. Training and validation alike call for accurate large-scale data sets comprising common scenarios, edge cases and every sort of variation in between. Furthermore, each process requires a distinct data set. We will explore how new techniques in synthetic data generation are helping time-pressured industries like automated driving satisfy the ever-growing need for larger, more diverse and highly accurate data sets.


Functional safety and security – partners or independent contributors?

Anamaria Hutuleac
Functional safety software assessor
NXP Semiconductors GmbH
As the complexity of systems increases, so does the need for functional safety and security. Quite often these goals are meant to be fulfilled by the same component. But how are they analyzed and designed so that instead of being two independent areas, they contribute together toward the achievement of the system goals? The presentation will show the similarities and differences in terms of goals, processes, concepts and mechanisms when it comes to functional safety and security. It will also discuss the analysis that can be done to bring them together on the same page.


Combined model-based and AI architectures for safety and comfortable driving

Dr Son Tong
Senior research engineer
Siemens Digital Industries Software
This talk presents our autonomous vehicle (AV) algorithm developments exploiting combination architectures of model-based and artificial intelligence (AI) toward safety and comfortable driving objectives. Recently, AI has been investigated in AV control; however, the disadvantage is a lack of rigorous results on explainability and safety. We discuss several strategies that incorporate data learning in control developments dealing with these challenges while enhancing performance: imitation learning of human-like driving in lane-keeping; Gaussian process control for snowy driving; reinforcement learning control. Finally, our experience in applying AI in robust sensor fusion is also highlighted.


Tackle the challenges of AD algorithm development with versatile software tools

Nicolas du Lac
Software has become an essential milestone for autonomous vehicles; at the same time, the number of software tasks is increasing rapidly with new sensor technologies. Automated driving development is becoming more complex, expensive and time-consuming. It is critical for developers to find reliable, versatile, powerful software tools that enable them to collaborate, face all these challenges by reducing the workload, and handle most automotive use cases from R&D to production. This presentation will focus on software development concepts for automated driving and will show how some OEMs and Tier 1 suppliers succeed, with algorithm demonstrations in multi-threaded and distributed architecture.


Migrating autonomous software from prototype to production

Robert Day
Director automotive solutions and platforms embedded and automotive division
This session discusses the challenges in moving autonomous vehicles from prototype to deployment, starting with the general requirements for the compute platform and then looking in more detail at the huge task of migrating the enormous software stack into a commercially deployable platform. The presentation will cover software platforms from an open-source and a commercial perspective, including the use of operating systems and hypervisors, middleware and application stacks. There will also be discussion around functional safety and how mixed-criticality systems can be deployed effectively.


Dynamic risk management (DRM) for safe and driver-accepted autonomous driving

Thomas Freudenmann
EDI GmbH - Engineering Data Intelligence
With our EDI hive IoT framework we trained an AI-based dynamic risk management (DRM) system – algorithms that enable autonomous vehicles to react to critical driving situations in a context-sensitive way like a human, experienced driver. The AI-based algorithms were trained with more than 100,000 incidents specifically focused on the drivers’ decision-making processes in critical traffic situations. In addition, we have established a validation environment with PTV Vissim in order to be able to secure different driver models in the simulation.


Real-time trajectory planning for automated driving and some related applications

Dr Joshué Pérez Rastelli
Principal investigator
Automated driving has increased the functionalities for a semi, highly, and fully automated vehicles in recent years. Most of the applications receive onboard sensors and communication inputs from the infrastructure and other vehicles. Some motion planning and control techniques have been implemented for complex environments; however, most of them spend a lot of time in the execution. This presentation describes the techniques used by different research teams, their contributions to motion planning and comparison among these techniques. Furthermore, an approach based on a testing methodology for validation of path planning and control algorithms for current and future automated vehicles is presented. A high degree of modularity, adaptability and real-time generation has been considered in the design of the proposed method. It shows good results for real applications in some complex environments.


Design and implementation of a novel lane-departure detection algorithm

Dr Imran Hayee
University of Minnesota Duluth
The existing lane-departure detection systems use either some kind of image processing or advanced differential GPS technology. We have designed a novel algorithm that can detect an unintentional lane departure using standard GPS technology without any need for a camera or lane-level resolution maps. We have successfully implemented such an algorithm in a proof-of-concept system and published our results in TRB 2019. Our algorithm success rate is almost 100%. We have filed a US patent application and have secured funding to develop a smartphone app to make this feature widely available to the public.


Multi-sensor AI considerations

Nicola Croce
Technical program manager
Deepen AI
Accurate classification and segmentation across multiple sensors is required for developing critical ADAS and autonomous vehicle components. Having redundant sensors is important to avoid safety risks in perception, tracking and path/motion planning algorithms. This talk will cover best practices for how to manage and benchmark your AV/ADAS AI models for fused sensor configurations. It will include data validation aspects, early versus late fusion, and data taxonomy implications for your model.


Raw sensor fusion – environmental perception model

Ronny Cohen
Co-founder and CEO
L2 and L2+ are becoming mainstream and are implemented in many vehicles today. However, the switch to L3 and above is extremely problematic since the responsibility moves from the driver to the AV. This means that the AV must be safer than the human driver (and many would say at least 10 times safer). To meet these functional safety requirements, sensor fusion algorithms must become much more precise, reliable, robust and with multiple redundancies. To achieve this, raw sensor fusion methodology must be implemented. Unlike object-level fusion, raw sensor fusion methodology can achieve much better precision, reduce false alarms, work with multiple sensor inputs and continue working even when some sensors degrade in performance or go completely offline.


Challenges of large-scale sensor data processing for autonomous driving

Jan Wiegelmann
Autovia GmbH
During the development and validation of autonomous driving systems, engineers must collect and store a huge amount of sensor data for analysis, deep learning and safety validation. In the presentation, we will show insights from using frameworks for large-scale data processing and distributed applications running in on-premises clusters and in the cloud. We will share our experiences and lessons learned on accelerating the end-to-end engineering process from data ingest and cataloging to analysis, development and safety validation.


Processing sensor data with a hybrid multi-protocol scale-out file system

Stefan Radtke
Technical director systems engineering, EMEA
Sensor data for autonomous vehicle development is growing exponentially. Large OEMs and suppliers typically have 10s of petabytes stored in their data centers. However, machine learning and other technologies drive demand in compute power that can often not be satisfied within the data centers. Training deep neural networks, for example, requires thousands of compute hours, which can often be better performed in the cloud where bursty workloads can be satisfied with on-demand resources such as GPU optimized servers. During the presentation, we’ll explain how Qumulo addresses these challenges with its multi-protocol file system that works in data centers as well as in the cloud.


How reinforcement learning can boost the way to fully automated driving

Christian Spohn
Innovation manager automotive
Atos Information Technology GmbH
Dr Christian Peeren
Senior data scientist - information management and analytics
Atos Information Technology GmbH
An important aspect of perfecting autonomous driving is providing labeled quality data to feed the numerous deep-learning algorithms. Since the amount of data is in the petabyte range, labeling is a very sophisticated task and mostly done manually. Due to its nature, reinforcement learning doesn't need fully labeled data, but rather quantity and the reward of teaching a neural network certain actions. In this presentation, we discuss whether reinforcement learning can help speed up the labor-intensive data curation process.


AI-in-the-loop optimization of Ford’s Predictive Dynamic Bending Light

Aleksander Spychala
Software engineer
Automotive lighting technologies have a challenging task to offer functionalities that provide a recognizable benefit to drivers. Engineering such technologies is even more difficult. Having successfully implemented artificial intelligence to quantify drivers’ subjective impressions of the performance of Predictive Dynamic Bending Light in real time, as well as in simulations, Ford has employed it for AI-assisted automated feature tuning by conducting AI-in-the-loop tests in addition to driver-in-the-loop tests. Using experimental design and multi-dimensional optimization techniques, various parameter sets are tested and gradually optimized with the aid of genetic algorithms to present a feature calibration offering the best performance determined by the AI.


UAVs for safety validation and development of highly automated driving

Julian Bock
Manager artificial intelligence
fka GmbH
Automated driving relies heavily on data-driven methods. Large datasets of real-world measurement data in the form of road user trajectories are crucial for several tasks. Using a drone has the major advantage of recording naturalistic behavior. Due to the ideal viewing angle, an entire scenario can be measured with significantly less occlusion than with sensors at ground level. Both the class and the trajectory of each road user can be extracted from the video recordings with high precision using state-of-the-art deep neural networks. Using this method, we are creating large-scale datasets with naturalistic road user behavior using camera-equipped drones.


Linux safety plan: how far are we now?

Dr Oscar Slotosch
Validas AG
After an introduction to basic safety concepts for tools, libraries and new software, we present an ISO 26262-compliant safety plan that can be applied to make Linux (and any other quality software) safe by completing its development. Completion is achieved by creating an 'interface' model for the software starting from requirements, architecture and test cases and verifying it using safety analysis and checklists. The process and the model can be applied up to ASIL D. Examples are taken from the Linux kernel source code and document the status of compliance of Linux.


Autonomous vehicles – upcoming UN ECE cybersecurity autonomous vehicle regulation

Dragos Dabija
Security manager
UNECE is at the center of the legal and regulatory work needed to realize the vision of new sustainable mobility and support the mass introduction of autonomous vehicles on the roads. It started dedicated work on this issue back in 2014. UNECE strongly contributes to enabling autonomous driving functionalities. The relevant forums (e.g. GRVA, WP.1 and WP.29) are following the technical progress with the aim of ensuring that the benefits of these new technologies can be captured without compromising safety and other progress achieved during the last decades (e.g. border crossing, interoperability, etc).


Establishing trust with in-vehicle software management

Roger Ordman
Executive vice president
Aurora Labs
Mass-market adoption of new technologies requires trust. Trust that the software underpinning the technology will be safe, secure and will constantly work as advertised. This presentation will look into the challenges faced by the vehicle manufacturers and their Tier 1 suppliers in ensuring that software faults and hacks can be detected before they cause a system failure. The regulatory landscape for cybersecurity and OTA will also be addressed (UNECE WP29 GRVA).


Over-the-air updates for the entire vehicle

Reiner Duwe
Regional manager, EMEA
Real-Time Innovations (RTI)
Traditional over-the-air updates are known for delivering updates to the system software alone. This presentation will introduce a secure methodology to now also update the functionality of the system hardware controllers. The demo will show how hardware updates can instantaneously be processed in all nodes in today's connected vehicle. The demo will show integrated technologies from the RTI Connext Drive framework, Xilinx adaptable technology and a secure cloud-based system from Bosch Software Innovations.


Lessons learned and mistakes that can be prevented in cybersecurity

Miguel Bañón
Vice president business line cybersecurity
The autonomous vehicle is an exciting technological beast that will disrupt transportation. Other technological advances have had much longer development cycles, from the proper IT building blocks, like operating systems, to space and aviation systems. From the perspective of an IT security evaluation facility, we observe a number of repeating flaws and mistakes that can be prevented, providing a sound basis for lessons that need to be learned and applied for secure take-off and success of the autonomous vehicle.


Protecting mobility: Tesla’s vulnerability can exist in all vehicles

Yonatan Zur
CEO and co-founder
Regulus Cyber
Researchers from Regulus Cyber recently initiated remote spoofing attacks on the Tesla GNSS (GPS) receiver, exploiting security vulnerabilities in mission-critical telematics, sensor fusion and navigation capabilities. With Tesla as a backdrop, the presentation will explore the security requirements for safe satellite-based navigation for driverless technology – cyber defense for sensors, anti-interference, anti-jamming, anti-spoofing systems that exist, and best practices for implementation. It will also discuss recent technological developments creating new threats, and will highlight the rapid growth of real-world attacks happening across these multiple sectors and how they are expected to grow as GNSS-dependent systems become more connected and autonomous.

Please note: this conference programme may be subject to change


Autonomous Vehicle International magazine