From Simulation Data to Test Cases for Fully Automated Driving and ADAS

Part 3: Practical Applications International audience Within this paper we present a new concept on deriving test cases from simulation data and outline challenging tasks when testing and validating fully automated driving functions and Advanced Driver Assistance Systems (ADAS). Open questions on topics like virtual simulation and identification of relevant situations for consistent testing of fully automated vehicles are given. Well known criticality metrics are assessed and discussed with regard to their potential to test fully automated vehicles and ADAS. Upon our knowledge most of them are not applicable to identify relevant traffic situations which are of importance for fully automated driving and ADAS. To overcome this limitation, we present a concept including filtering and rating of potentially relevant situations. Identified situations are described in a formal, abstract and human readable way. Finally, a situation catalogue is built up and linked to system requirements to derive test cases using a Domain Specific Language (DSL). Document type: Part of book or chapter of boo

Paving the Roadway for Safety of Automated Vehicles: An Empirical Study on Testing Challenges

The technology in the area of automated vehicles is gaining speed and promises many advantages. However, with the recent introduction of conditionally automated driving, we have also seen accidents. Test protocols for both, conditionally automated (e.g., on highways) and automated vehicles do not exist yet and leave researchers and practitioners with different challenges. For instance, current test procedures do not suffice for fully automated vehicles, which are supposed to be completely in charge for the driving task and have no driver as a back up. This paper presents current challenges of testing the functionality and safety of automated vehicles derived from conducting focus groups and interviews with 26 participants from five countries having a background related to testing automotive safety-related topics.We provide an overview of the state-of-practice of testing active safety features as well as challenges that needs to be addressed in the future to ensure safety for automated vehicles. The major challenges identified through the interviews and focus groups, enriched by literature on this topic are related to 1) virtual testing and simulation, 2) safety, reliability, and quality, 3) sensors and sensor models, 4) required scenario complexity and amount of test cases, and 5) handover of responsibility between the driver and the vehicle.Comment: 8 page

ENABLE-S3 – Advanced V&V technologies and methods combined with simulation and testing environments enable the safe and secure development of Autonomous Vehicles

Highly automated and autonomous transport is a technology field that enables safer and cleaner transport and unburdens the driver from boring and/or error prone driving task. The development of automated transport features and vehicles will or have already led to new business opportunities in many technology sectors, like sensor technologies, SW-development or mobility services to name just a few of them. The highly precise sensors and communication technologies as well as the necessary computing power and algorithms within the vehicle plus the digital infrastructure that are necessary to realize the autonomous transport are developing very fast. But this goes also along with new heavy-weight challenges in terms of safety and security aspects. Extensive verification and validation efforts are necessary to make automated systems at least as safe as human-operated systems are nowadays. The ENABLE-S3 project develops verification and validation technologies and methods that will help to tackle this challenge with reasonable efforts and high coverage of test-cases. 71 partners from different transport sectors (automotive, aerospace, rail, maritime, farming) and other industries are creating new knowledge in the areas of testing and simulation methods & technologies as well as the required testing platforms and environments. Research within ENABLE-S3 focuses on: - Test and simulation environments supporting open standards (e.g. Functional Mock-up Interface, OpenSimulationInterface) wherever possible in order to run tests for automated transport seamlessly in different virtual and semi-virtual environments. - Open standards for the definition, management and execution of test cases/testing scenarios like OpenScenario or OpenDrive and their relationship to other existing standards like ASAM-XiL. - Investigation of testing methodologies which are necessary to reduce the number of test cases tremendously, among them are DoE (design of experiments), combinatorial testing, FMEA analysis etc. - Development of sensor models as well as sensor stimuli (physical sensor signal generators). - Generation of test cases out of existing recorded real-world data. The developed methods are applied in different industrial use-cases. This paper will give an overview over the needed building blocks for testing AD functions, including scenario generation, test planning, and test execution and simulation that were already developed within the ENABLE-S3 project and will finally present a practical use case and the application of aforementioned methods to an ACC function of a vehicle. The results gained so far in the project will show that the verification and validation methods combined with simulation and testing technologies for automated vehicles in transport play a major role in reaching the high safety and security levels that end customers and legal authorities will demand for this important technology in order to get acceptance and in order to provide a great step forward in reducing road fatalities and at the same time also CO2 emissions

Product Development within Artificial Intelligence, Ethics and Legal Risk

This open-access-book synthesizes a supportive developer checklist considering sustainable Team and agile Project Management in the challenge of Artificial Intelligence and limits of image recognition. The study bases on technical, ethical, and legal requirements with examples concerning autonomous vehicles. As the first of its kind, it analyzes all reported car accidents state wide (1.28 million) over a 10-year period. Integrating of highly sensitive international court rulings and growing consumer expectations make this book a helpful guide for product and team development from initial concept until market launch