A Review of Testing Object-Based Environment Perception for Safe Automated Driving
It addresses the problem of ensuring safety in automated driving systems for developers and regulators, but is incremental as it reviews existing work without proposing new solutions.
This paper reviews literature on testing object-based environment perception for safe automated driving, focusing on test criteria, scenarios, and reference data, and finds that safety-aware perception testing remains an open issue with unsolved challenges.
Safety assurance of automated driving systems must consider uncertain environment perception. This paper reviews literature addressing how perception testing is realized as part of safety assurance. We focus on testing for verification and validation purposes at the interface between perception and planning, and structure our analysis along the three axes 1) test criteria and metrics, 2) test scenarios, and 3) reference data. Furthermore, the analyzed literature includes related safety standards, safety-independent perception algorithm benchmarking, and sensor modeling. We find that the realization of safety-aware perception testing remains an open issue since challenges concerning the three testing axes and their interdependencies currently do not appear to be sufficiently solved.