Automated driving technology is developing at an unprecedented rate – a quick response is needed from regulators in order not to obstruct innovation and at the same time ensure market access to safe automated vehicles (AVs). The introduction of innovative certification approaches is required to assess and verify AVs safety, complementing conventional physical testing in order to ensure safe vehicle performance in real life. This presentation will introduce the proposed EU method for automated driving, including audit of the manufacturer design/development process, verification through track and on-road testing, and operational feedback from real-life experience after market introduction.

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 cost reduction; assessment – creation of assessment protocols increasing vehicle safety awareness; certification – support of regulations ensuring the safe introduction of CAD technologies to the market.

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.

The presentation will address the key challenges of safety validation of automated and connected vehicles from a methodological and standardization perspective. It will also deal with the need for and setup of an alliance between key standardization institutions and third-party technical service providers as well as technology and infrastructure developers, providers and operators. The latest outcomes as well as further focus areas and topics of the alliance will also be presented.

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 a 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.

Development of AD/ADAS is a massively complex process, requiring the management of vast data sets. In turn, this leads to subsequent iterative development using HiL/SiL performance testing against defined test cases, and the development of real-time inference engines. A growing development ecosystem, including toolchains, algorithms, public training decks, annotation services and more is coming but requires carefully managed security and access to highly valuable, company-confidential information. An approach that offers access to multiple public cloud services at the lowest cost but with the greatest agility is essential to take a leading position in this highly competitive market. This webinar will discuss best-in-class methodologies for leveraging public cloud, shedding light on the tradeoffs and technical roadblocks introduced and how to avoid them.

A fundamental challenge for the autonomous vehicle industry is how to deliver quality training and test data more quickly and cost-efficiently. rFpro brings the real world to simulation, where our highly accurate digital models provide the perfect environment for training, testing and validating AV and ADAS systems. What if you then need to expand your data production and deliver synchronous simulation across multiple sensors? One of our approaches, Data Farming, is being employed by Tier 1 suppliers and AV technology providers, including Denso and Ambarella, to achieve exactly that. Join us to hear more about how this approach is being successfully trialed with industry leaders.

Ensuring ADS are safe is a major challenge for the broad deployment of automated driving systems. Emerging standards and regulations will help direct the efforts of ADS developers in testing, scenario description and more, but how can we collect viable safety metrics, expose the unknown unknowns and comply with new regulations that recommend a combination of physical and massive-scale virtual testing? This presentation will describe these challenges and offers pragmatic tools along with coverage-driven verification methodology as a way to supply quantitative safety metrics, expose unknown unknowns, describe scenarios in a reusable and manageable way, and more. It will demonstrate the use of coverage-driven verification methodology and the Foretify platform for the implementation of the new ALKS regulation package.

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 into account an array of 'in-the-field' situations.

New forms of cooperation are necessary to turn the vision of autonomous driving and fully connected mobility systems into 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.

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.

One of the main challenges of validating ADAS and AV systems is analyzing the data mountains of real drives to identify the gaps in the data. When the gaps are identified, it is possible to synthetically generate the missing data to achieve full coverage for training and validation of ADAS and AV stacks. In this session we will explore how we ingest real data in order to find those gaps, and how we parameterize our simulation platform to generate meaningful synthetic data for perception, prediction and control stacks.

ADAS/AD providers use artificial intelligence as one key component. Researchers and developers who can deliver insights faster while managing rapid infrastructure growth will be poised to be industry leaders. Following the process of data acquisition from test drives via ingest to the data center and processing of big data, many challenges occur. To handle this unstructured data, seamless scaling systems and toolsets are necessary from the sensor to the data center with storage and computing systems. This combined presentation provides a case study of how synchronized data capturing in test drives, big data computing, storage and archiving are integrated in today’s ADAS/AD development workflow.

Execution of hundreds of thousands of test runs with a trustworthy virtual vehicle in a cluster or cloud environment is a challenge in itself. With Simcenter Prescan360, we introduced an off-the-shelf solution to generate scenario variants, orchestrate the massive numbers of test runs and post-process these results for easy interpretation. However, by adding additional smartness, the process can be optimized and automated much further. In this presentation we will highlight how design exploration and AI techniques are applied to create critical but unseen scenarios that might make the system fail. We will also introduce smart metrics to automate accurate safety assessments of the automated driving functional performance.

As autonomous vehicle programs prepare to deploy their systems on public roads, it is critical to ensure that the systems have been fully tested against the requirements, and that there’s complete traceability of the test results. Furthermore, testing and managing technical risks becomes more challenging as the programs work with multiple third-party vendors/partners. This discussion explores approaches to requirements testing, traceability, and using simulation to integrate multiple partners and make safety cases.