We present an approach to assess the risk taken by an individual vehicle during on-road driving. The driving risk is defined as a Surrogate Measure of Safety characterising a conflict event: a safety-critical situation that occurs prior to a crash event. The assessment approach is developed within the framework of artificial field theory, envisioned for safety analysis and design of driving (support/automation) applications. Here, any obstacle (neighbouring entity on road) to the subject vehicle is treated as a finite scalar risk field that is formulated in the predicted configuration space of the subject vehicle. The driving risk estimate is the strength of the risk field at the subject vehicle’s location. This risk field is formulated as the product of two factors: expected crash energy (as an approximation of consequences) and the collision probability. The collision probability with a movable obstacle (vehicle) is estimated based on probabilistic motion predictions. The subject and neighbouring vehicle’s possible positions at discrete future time steps are predicted. Thereby, the risk can be assessed for a single time step or over multiple future time steps, depending on the required temporal resolution of the estimates. The properties of the risk estimates are mathematically evaluated. We applied the single step approach to assessing the driving risk in three near-crash situations selected from a naturalistic dataset. The risk description qualitatively reflects the narration of the situation. Additionally, we applied the multi-step approach to estimate the risk along four possible trajectories while approaching a lane drop section. The risk estimates along the trajectory plans clearly marked the safest trajectory. The results of both example sets show that the risk trends, in general, are consistent with Time To Collision (a prominent surrogate measure of safety). The proposed risk estimate provides a better basis to assess the driving safety of an individual vehicle by considering the uncertainty over the future ambient traffic state and magnitude of expected crash consequences. Therefore, the proposed driving risk model could potentially be used as a component of integrated vehicle safety applications and as a supplementary surrogate measure for assessing the driving safety.
Original languageEnglish
Number of pages37
Publication statusUnpublished - 2019

ID: 57216573