AAC 2022 Paper Abstract

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Paper TuBT3.1

Weng, Bowen (Transportation Research Center Inc.), Zhu, Minghao (The Ohio State University), Keith, Redmill (The Ohio State University)

A Formal Safety Characterization of Advanced Driver Assist Systems in the Car-Following Regime with Scenario-Sampling

Scheduled for presentation during the Regular Session "Position, Navigation, and Timing Security in Highly Automated Vehicles" (TuBT3), Tuesday, August 30, 2022, 15:00−15:20, Ballroom

10th IFAC International Symposium on Advances in Automotive Control, August 28-31, 2022, Columbus, Ohio, USA

This information is tentative and subject to change. Compiled on March 28, 2024

Keywords Testing and validation, Advanced Driver Assist Systems

Abstract

The capability to follow a lead-vehicle and avoid rear-end collisions is one of the most important functionalities for human drivers and various Advanced Driver Assist Systems (ADAS). Existing safety performance justifications of car-following systems either rely on simple concrete scenarios with biased surrogate metrics or require a significantly long driving distance for risk observation and inference. In this paper, we propose a guaranteed unbiased and sampling efficient scenario-based safety evaluation framework inspired by previous work on the almost safe set quantification. The proposal characterizes the complete safety performance of the test subject vehicle in the car-following regime. The performance of the proposed method is also demonstrated in challenging cases including some widely adopted car-following decision-making modules and the commercially available Openpilot driving stack by CommaAI.

 

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