A Review of Testing Object-Based Environment Perception for Safe Automated Driving
arXiv:2102.08460 · doi:10.1007/s42154-021-00172-y
Abstract
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.
23 pages, 7 figures. Automotive Innovation (2022)
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Cited by in corpus (5)
- PEM: Perception Error Model for Virtual Testing of Autonomous Vehicles
- CODEI: Resource-Efficient Task-Driven Co-Design of Perception and Decision Making for Mobile Robots Applied to Autonomous Vehicles
- A Systematic Literature Review on Safety of the Intended Functionality for Automated Driving Systems
- State of the Art Study of the Safety Argumentation Frameworks for Automated Driving System Safety
- Anticipating Accidents through Reasoned Simulation