From the latest ADAS technologies to full autonomy

Synopsis: 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.

Synopsis: 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.

Synopsis: 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.

Synopsis: 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.

Synopsis: 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.

Synopsis: 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.

Synopsis: 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.