Continuous Risk Measures for Driving Support

• URL: http://arxiv.org/abs/2303.08007v1
• Date: Tue, 14 Mar 2023 15:54:37 GMT
• Title: Continuous Risk Measures for Driving Support
• Authors: Julian Eggert and Tim Puphal
• Abstract summary: We compare three model-based risk measures by evaluating their stengths and qualitatively testing them quantitatively. We derive a novel risk measure based on the statistics of sparse critical events and so-called survival conditions. The resulting survival analysis shows to have an earlier detection time crashes and less false positive detections in near-crash and non-crash cases supported by its solid theoretical grounding.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we compare three different model-based risk measures by evaluating their stengths and weaknesses qualitatively and testing them quantitatively on a set of real longitudinal and intersection scenarios. We start with the traditional heuristic Time-To-Collision (TTC), which we extend towards 2D operation and non-crash cases to retrieve the Time-To-Closest-Encounter (TTCE). The second risk measure models position uncertainty with a Gaussian distribution and uses spatial occupancy probabilities for collision risks. We then derive a novel risk measure based on the statistics of sparse critical events and so-called survival conditions. The resulting survival analysis shows to have an earlier detection time of crashes and less false positive detections in near-crash and non-crash cases supported by its solid theoretical grounding. It can be seen as a generalization of TTCE and the Gaussian method which is suitable for the validation of ADAS and AD.

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