Investigating the Configurations of an Industrial Path Planner in Terms of Collision Avoidance

Typical approaches to test Autonomous Driving Systems (ADS) generate tests in a simulation environment. A common goal in ADS testing is to find scenarios in which the car collides, as these could witness ADS faults. Recent approaches not only find a collision, but they also show whether it could be avoided: they search for a different ADS configuration (i.e., the setting of some parameters) using which the car does not collide. However, such techniques do not explain why the collision occurs and why the alternative configuration is able to avoid it. In this paper, we propose an approach to investigate the relationship between the ADS configurations and the obtained safety during driving. We first use a technique based on fuzzification to partition ADS parameters in different categories, and a spectra- based analysis to identify which categories relate to hazard and safety. Then, we consider collision scenarios by inspecting how the different ADS configurations affect the driving characteristics (e.g., acceleration and curvature) and, so, cause or avoid a collision. We applied the approach to the path planner of our industry partner, by considering three traffic situations. We observed that the path planner, to guarantee safety, should be configured differently in different situations.