Dilemma of Responsibility-Sensitive Safety in Longitudinal Mixed Autonomous Vehicles Flow: A Human-Driver-Error-Tolerant Driving Strategy

Qi, Hongsheng · 2024 · DOAJ

DOI: 10.1109/OJITS.2024.3397959

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Summary

This paper addresses the safety challenges of autonomous vehicles (AVs) operating in mixed traffic flows alongside human-driven vehicles (HDVs). While Responsibility-Sensitive Safety (RSS) provides a formal framework for AV safety, it fails to guarantee collision avoidance when HDVs violate safety rules. The authors identify a specific failure mode termed the "dilemma," where an AV is forced to choose between crashing into its leader or its follower if the follower violates RSS spacing rules. This concept is generalized to "polylemma" for platoons of arbitrary length, where a single HDV violation inevitably causes at least one crash among neighboring vehicles. The study aims to resolve this by proposing a Human-Error-Tolerant (HET) driving strategy that allows AVs to accommodate human driver errors. The methodology involves defining the dilemma and trilemma scenarios mathematically and deriving critical distance thresholds for their avoidance. The proposed HET strategy requires AVs to maintain an additional safety gap and utilize moderate deceleration rates, distinct from standard RSS parameters, to create buffer space for errant HDVs. To evaluate the strategy, the authors analyze real-world trajectory data to calculate the probability of polylemma occurrences and quantify the resulting risk reduction and traffic capacity variations. The analysis incorporates market penetration rates (MPR) to assess the strategy's impact on mixed traffic flows under various speed conditions. The results demonstrate that the HET strategy significantly mitigates risks associated with human error. Specifically, the analysis indicates that at a 50% market penetration rate of AVs, the strategy reduces risks due to human error by 80%. However, this safety improvement comes with a trade-off in traffic capacity, which decreases depending on the background traffic flow speed. The study provides specific calculations for the ratio and probability of polylemma scenarios in current traffic datasets, establishing the quantitative baseline for the proposed avoidance strategy. The significance of this work lies in its practical approach to AV safety in heterogeneous traffic environments. By acknowledging that HDVs cannot be controlled directly, the paper shifts the focus to leveraging AV behavior to indirectly reduce systemic risk. The introduction of the polylemma concept and the HET strategy offers a concrete method for AVs to avoid unavoidable collision scenarios caused by human violations. This contributes to the broader field of intelligent transportation systems by providing a verified, error-tolerant longitudinal driving strategy that balances safety assurance with traffic efficiency, supporting the wider adoption of autonomous vehicles.

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