A Finite-Sampling, Operational Domain Specific, and Provably Unbiased Connected and Automated Vehicle Safety Metric

• Published in: IEEE transactions on intelligent transportation systems Vol. 24; no. 6; pp. 6650 - 6662
• Main Authors: Weng, Bowen, Capito, Linda, Ozguner, Umit, Redmill, Keith
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Automation; connected and automated vehicle; Empirical analysis; invariant set; Lead; Measurement; operational design domain; Roads; Safety; Safety metric; Shape; Simulator fidelity; Simulators; Testing; Traffic safety; Vehicle dynamics; Vehicle safety
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2022.3164358

A connected and automated vehicle safety metric determines the performance of a subject vehicle (SV) by analyzing the data involving the interactions among the SV and other dynamic road users and environmental features. When the data set contains only a finite set of samples collected from the naturalistic mixed multi-modal traffic driving environment, a metric is expected to generalize the safety assessment outcome from the observed finite samples to the unobserved cases by specifying in what domain the SV is expected to be safe and how safe the SV is, statistically, in that domain. However, to the best of our knowledge, none of the existing safety metrics is able to justify the above properties with an operational domain specific, guaranteed complete, and provably unbiased safety evaluation outcome. In this paper, we propose a novel safety metric that involves the <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula>-shape and the <inline-formula> <tex-math notation="LaTeX">\epsilon </tex-math></inline-formula>-almost robustly forward invariant set to characterize the SV's almost safe operable domain and the probability for the SV to remain inside the safe domain indefinitely, respectively. The empirical performance of the proposed method is demonstrated in several different operational design domains through a series of cases covering a variety of fidelity levels (real-world and simulators), driving environments (highway, urban, and intersections), road users (car, truck, and pedestrian), and SV driving behaviors (human driver and self driving algorithms).