Stress Testing Method for Scenario-Based Testing of Automated Driving Systems

Abstract

Classical approaches for testing of automated driving systems (ADS) of SAE levels 1 and 2 were based on defined scenarios with specific maneuvers, depending on the function under test. For ADS of SAE level 3+, the scenario space is infinite and calling for virtual testing and verification. The biggest challenge for virtual testing methods lies in the realistic representation of the virtual environment where the ADS is tested. Such an environment shall provide the possibility to model and develop vehicles, objects, control algorithms, traffic participants and environment elements in order to generate valid and representative test data. An important and crucial aspect of such environments is the testing of vehicles in a complex traffic environment with a stochastic and realistic traffic representation. For this research we used a microscopic traffic flow simulation software (TFSS) PTV Vissim and the vehicle simulation software IPG CarMaker to test ADS. Although the TFSS provides realistic and stochastic behavior of traffic participants, the occurrence of safety-critical scenarios (SCS) is not guaranteed. To generate and increase such scenarios, a novel stress testing method (STM) is introduced. With this method, traffic participants are manipulated in the vicinity of the vehicle under test in order to provoke SCS derived from statistical accident data on motorways in Austria. Using the co-simulation between IPG CarMaker, PTV Vissim and external driver models in Vissim are used to imitate human driving errors, resulting in an increase of SCS.

Keywords

• Automatic testing
• autonomous vehicles
• scenario generation
• ADAS