An Incremental Approach for Understanding Collision Avoidance of an Industrial Path Planner

Abstract

Autonomous Driving Systems (ADSs) are complex systems that must consider different aspects such as safety, compliance to traffic regulations, comfort, etc. The relative importance of these aspects is usually balanced in a weighted cost function. However, there is generally no optimal set of weights, and different driving situations may require different weights values to guarantee a safe drive. Recent testing approaches can generate diverse driving scenarios in which different ADS configurations lead to various degrees of hazard. These tests need to be properly analyzed to improve the ADS's safety. In this paper, we propose \paolo{an analysis approach} that is able to assess the relation between the ADS configurations and the level of hazard that is obtained in some particular traffic situations. The approach uses fuzzification to partition ADS weights in different categories, and a spectrum-based analysis to identify which weights categories are related to hazard and safety. The occurrence of a hazard could be due to a single weight or to combinations of two or more weights. For scalability, the approach performs an incremental analysis, in which first single weights are considered, and then weight combinations of higher order. The approach has been applied to an industrial path planner.