Citation

Yan X, Feng S, Leblanc DJ, Flannagan CAC, Liu HX, Svenson AL. 27th International Technical Conference on the Enhanced Safety of Vehicles (ESV); April 3-6, 2023; Abstract #: 23-0224, pp. 10p. Washington, DC USA: US National Highway Traffic Safety Administration, 2023 open access.

Copyright

(Copyright © 2023 open access, US National Highway Traffic Safety Administration)

Abstract

27th International Technical Conference on the Enhanced Safety of Vehicles (ESV): Enhanced and Equitable Vehicle Safety for All: Toward the Next 50 Years

https://www-esv.nhtsa.dot.gov/Proceedings/27/27ESV-000224.pdf

Assuring safe operation remains as one of the main challenges for developing and deploying automated driving systems (ADS). Real-time safety risk metrics may play important roles in calculating a risk measure of driving situations. Although a number of safety metrics have been proposed previously, it is difficult to compare different safety metrics and assess their performance because different behavioral assumptions underly for each. In this paper, a method to assess the behavior of safety risk metrics by determining the subject vehicle (SV) situational safety using logged vehicle trajectory data is proposed. Specifically, it is examined whether the SV is in a collision unavoidable situation at each moment, given the near-future trajectories of all surrounding principal other vehicles (POVs) recorded in the dataset after this moment. The main benefit of using logged vehicle trajectory data is the elimination of behavior prediction errors caused by model assumptions and approximations. This establishes a ground truth for crash outcomes independent of the risk metrics. Using the proposed methodology, the performance of different real-time safety metrics can be evaluated using simulated and/or real-world vehicle trajectories. The proposed methodology also has the potential to be applied in scenarios with vulnerable road users (VRU) interactions. In the case study, three real-time safety metrics are considered: time-to-collision (TTC), the PEGASUS Criticality Metric (PCM), and the Model Predictive Instantaneous Safety Metric (MPrISM). The results can help practitioners to better understand the characteristics and applicability of different safety metrics for different situations. The evaluation results can also help researchers improve and refine existing safety metrics.

Language: en