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.

The increasingly used approach of combining different simulation softwares in testing of automated driving systems (ADSs) increases the need for potential and convenient software designs. Recently developed co-simulation platforms (CSPs) provide the possibility to cover the high demand for testing kilometers for ADSs by combining vehicle simulation software (VSS) with traffic flow simulation software (TFSS) environments. The emphasis on the demand for testing kilometers is not enough to choose a suitable CSP. The complexity levels of the vehicle, object, sensors, and environment models used are essential for valid and representative simulation results. Choosing a suitable CSP raises the question of how the test procedures should be defined and constructed and what the relevant test scenarios are. Parameters of the ADS, environments, objects, and sensors in the VSS, as well as traffic parameters in the TFSS, can be used to define and generate test scenarios. In order to generate a large number of scenarios in a systematic and automated way, suitable and appropriate software designs are required. In this paper, we present a software design for a CSP based on the Model–View–Controller (MVC) design pattern as well as an implementation of a complex CSP for virtual testing of ADSs. Based on this design, an implementation of a CSP is presented using the VSS from IPG Automotive (CarMaker) and the TFSS from the PTV Group (Vissim). The results showed that the presented CSP design and the implementation of the co-simulation can be used to generate relevant scenarios for testing of ADSs.

As the complexity of automated driving systemss (ADSs) with automation levels above level 3 is rising, virtual testing for such systems is inevitable and necessary. The complexity of testing these levels lies in the modeling and calculation demands for the virtual environment, which consists of roads, traffic, static and dynamic objects, as well as the modeling of the car itself. An essential part of the safety and performance analysis of ADSs is the modeling and consideration of dynamic road traffic participants. There are multiple forms of traffic flow simulation software (TFSS), which are used to reproduce realistic traffic behavior and are integrated directly or over interfaces with vehicle simulation software environments. In this paper we focus on the TFSS from PTV Vissim in a co-simulation framework which combines Vissim and CarMaker. As it is a commonly used software in industry and research, it also provides complex driver models and interfaces to manipulate and develop customized traffic participants. Using the driver model DLL interface (DMDI) from Vissim it is possible to manipulate traffic participants or adjust driver models in a defined manner. Based on the DMDI, we extended the code and developed a framework for the manipulation and testing of ADSs in the traffic environment of Vissim. The efficiency and performance of the developed software framework are evaluated using the co-simulation framework for the testing of ADSs, which is based on Vissim and CarMaker.

Considering the recent advances in autonomous driving technology, conventional on-road based testing tools cannot meet the validation requirements with respect to time and cost-efficiency. Scenario-based virtual validation methods offer an efficient virtual testing approach that supports the development and contribute to decreased on-road-testing. X- in-the loop testing methods contribute to stepwise increase in test/validation quality by introducing hardware/software elements starting from the component up to full vehicle level and have been increasingly integrated into the process of design and validation of intelligent vehicle systems. In this paper, a novel framework that includes a highly dynamic vehicle-in-the-loop test bed, a traffic flow simulation method is introduced to precisely assess the impact of specific variables on the performance of an intelligent system with all vehicle components included.

The safety approval and assessment of automated driving systems (ADS) are becoming sophisticated and challenging tasks. Because the number of traffic scenarios is vast, it is essential to assess their criticality and extract the ones that present a safety risk. In this paper, we are proposing a novel method based on the time-to-react (TTR) measurement, which has advantages in considering avoidance possibilities. The method incorporates the concept of fictive vehicles and variable criticality thresholds (VCTs) to assess the overall scenario’s criticality. By introducing variable thresholds, a criticality scale is defined and used for criticality calculation. Based on this scale, the presented method determines the criticality of the lanes adjacent to the ego vehicle. This is performed by placing fictive vehicles in the adjacent lanes, which represent copies of the ego. The effectiveness of the method is demonstrated in two highway scenarios, with and without trailing vehicles. Results show different criticality for the two scenarios. The overall criticality of the scenario with trailing vehicles is higher due to the decrease in avoidance possibilities for the ego vehicle.