Automotive Testing and Traffic Simulation

The challenge for the system under test varies largely with the aggressiveness of the traffic environment and the resulting interactions. Vissim Automotive provides easy set-up of driving behavior for environmental traffic. The user can switch between five pre-configured driving styles, from comfort oriented, over neutral, to aggressive. These styles will influence: 

• Acceleration/deceleration functions
• Configuration of conflict areas
• Curve speed functions
• Driver model properties (e.g. gap times, safety distances, accepted decelerations for lane changes etc.) 

In addition, it will allow for a global adjustment of the speed level and enable an easy stochastic use of different driver errors in order to trigger critical situations. 

The prevailing weather conditions have a major influence on driving behavior. To test the system under different conditions, PTV Vissim Automotive offers three different presets (neutral, bad, very bad) that can be easily selected and compared.

As an intuitive global modificatory, the presets will influence several aspects of driving behavior:

• Desired speed distributions
• Maximum deceleration functions
• Curve speed functions
• Gap time distributions
• Look ahead and look back distances
• Safety distances

PTV Vissim’s standard car following model reflects human behavior, including its imperfections for perception of distance and speed. However, more and more cars use Advanced Driver Assistance Systems (ADAS) or even automated driving (AD). They rely on sensors for perception and algorithmic control and thus behave differently. 

PTV Vissim Automotive therefore, in addition to the standard model, offers a new behavior model specifically designed to simulate automated driving states. It contains a generalized ACC implementation for longitudinal control and an ALC-based lane change model. The surrounding traffic, simulated by Vissim Automotive, can consist of only one, or a mixture of both behavior models[FP4.1] to reflect a mixed fleet of automated and human-driven vehicles with definable penetration rates of automation. 

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PTV Vissim Automotive supports the import of ASAM OpenDRIVE 1.8 network files (or older) for direct use for simulation. It automatically creates default traffic signal programs for running traffic lights for all intersections, where signal heads are described in the file. In addition to the import of road geometry and control elements like speed limits, priority rules or signal heads, the OpenDRIVE import from Vissim Automotive also enables the import of pedestrian infrastructure, such as sidewalks and crosswalks. They are set-up to be used by “Viswalk”-pedestrians with a true area-based behavior for the most realistic representation of movement and interactions between pedestrians. 

The OpenDRIVE import also includes the support of embedded OpenCRG files for the description of local road surface anomalies like e.g. a speed hump or a pothole. Corresponding reduced speed areas are placed in the correct position of the network to trigger the necessary adaption of the driving speed of the vehicles. In addition, PTV Vissim Automotive supports the export of critical driving scenarios in OpenSCENARIO XML for re-simulation in other automotive simulation tools. This export includes the trajectories of the vehicles directly involved in the critical incidents, as well as those of surrounding vehicles and pedestrians within a defined radius around the incident. The length of the scenario can also be specified as a time envelope around the incident.

For the use in ADAS/ AV development, we have equipped PTV Vissim Automotive with powerful options like aggressive driving presets and statistically distributed driver errors like inattention to create many critical driving situations in short simulation time to ensure a challenging and meaningful test-environment. The in-built detection of critical incidents is a great addition to this function. It allows you to create a report about the simulation and to use this information for further analysis or re-simulation. 

This incident detection can identify actual crashes of simulated vehicles as well as other critical situations like harsh decelerations or near misses on basis of critical time to collision (TTC) values. It creates a meaningful protocol about all incidents with data about the involved vehicles, their relative speeds, orientations etc. This allows for further filtering to e.g. only severe crashes or specific crash types.