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

Virtuelle Realität für Radargeräte in Autos

Car manufacturers spend quite a lot on the development of driver assistance systems and subsequently on autonomous driving functionality. To ensure the safety and reliability of these functions meet industrial standards it is necessary to verify and validate their functionality. While tests on the road are still the ultimate evidence of correct operation they are associated with huge efforts and risks. Therefore, they have to be complemented by other means like simulations and tests on specialised testbeds. For the latter the car’s sensors have to be stimulated in a way that they perceive a desired – but only virtual – environment. An important type of sensor in cars is the radar due to its various advantages. This article describes the development of a stimulator generating virtual radar targets in order to enable the testing of autonomous driving functions. Document type: Articl