Continuous Risk Measures for Driving Support
This work addresses the need for improved risk assessment in advanced driver assistance systems (ADAS) and autonomous driving (AD), though it appears incremental as it builds on existing measures like TTC.
The paper tackles the problem of evaluating risk measures for driving support by comparing three model-based approaches, including a novel survival analysis method that achieves earlier crash detection and fewer false positives in near-crash and non-crash cases, with quantitative testing on real scenarios.
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.