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.

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.

Due to the high degree of connectivity of highly automated and autonomous vehicles, they must function in a variety of scenarios during the utilization phase. Providing a realistic test environment that covers these usage scenarios is a major challenge for virtual engineering of autonomous driving functions. Especially for the virtual validation of the driving functions, this requires a large number of simulation models of external systems involved, to which not every developer has access. However, AI procedures can possibly be used in future to reduce the effort required to generate the necessary simulation models.

The verification and validation (V&V) of system safety are among the greatest challenges in the autonomous vehicle (AV) industry. For the associated homologation process, it is essential to bridge the gap between the industry's expertise and the regulators’ desire to define requirements and policies that safeguard AV development. Based on several years' experience that dSPACE has gained in the context of hardware- and software-in-the-loop simulation as well as release testing, a proposal for a comprehensive end-to-end V&V process is presented.

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!

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.

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.

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.

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.

Use cases for Level 3 and 4 active safety and autonomous driving validation are skyrocketing, and the number of required certification scenarios is growing exponentially. Since vehicles are the anchor of these simulations, it is imperative to leverage a high-fidelity model that can scale into all simulation domains without changing simulation methodologies. This presentation will demonstrate how high-fidelity, OEM-validated solvers can be integrated into desktop simulation tools and easily be migrated to hardware-in-the-loop systems.

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.

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.

To pave the ground for scalable 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 scalable 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.