Probabilistic Uncertainty-Aware Risk Spot Detector for Naturalistic Driving
This work addresses risk assessment for autonomous vehicles, offering a more comprehensive model that handles real-world uncertainties, though it appears incremental as it builds on existing risk metrics.
The paper tackled the problem of risk assessment for autonomous vehicles by developing a probabilistic situation risk model that incorporates sensory, temporal, and behavioral uncertainties, resulting in a Risk Spot Detector that is more selective and specific in predicting risk compared to traditional metrics like Time Headway and Time-To-Contact.
Risk assessment is a central element for the development and validation of Autonomous Vehicles (AV). It comprises a combination of occurrence probability and severity of future critical events. Time Headway (TH) as well as Time-To-Contact (TTC) are commonly used risk metrics and have qualitative relations to occurrence probability. However, they lack theoretical derivations and additionally they are designed to only cover special types of traffic scenarios (e.g. following between single car pairs). In this paper, we present a probabilistic situation risk model based on survival analysis considerations and extend it to naturally incorporate sensory, temporal and behavioral uncertainties as they arise in real-world scenarios. The resulting Risk Spot Detector (RSD) is applied and tested on naturalistic driving data of a multi-lane boulevard with several intersections, enabling the visualization of road criticality maps. Compared to TH and TTC, our approach is more selective and specific in predicting risk. RSD concentrates on driving sections of high vehicle density where large accelerations and decelerations or approaches with high velocity occur.