Driving Risk Field Model and Its Application in Trajectory Planning: A New Perspective

Driving risk field (DRF) emerges as an effective way to assess the driving safety of connected and automated vehicles (CAVs). Most existing DRF models are established from the so-called birds-eye-view (BEV), which limits their accuracy for distributed vehicle-level tasks such as trajectory planning since the interactions between ego vehicle (EV) and its surrounding traffic environment have not been fully considered. To fill this research gap, we establish a novel DRF model from ego-vehicle-view (EVV) and apply it in trajectory planning in this paper. Firstly, the collision boundary between EV and its surrounding obstacles is defined by introducing the elliptical model to fully consider the geometry characteristics of vehicles. Secondly, the relative motion influence coefficient is designed to accurately characterize the relative motion between EV and obstacles, instead of using only basic driving state information such as location and velocity. On this basis, the unified DRF is established from EVV for driving safety assessment, which contains vehicle risk field (VRF) and lane marking risk field (LMRF). Based on the established DRF model, we then design a rolling trajectory planning method (RTPM) with a rolling horizon strategy, which not only ensures a long prediction horizon but also effectively reduces the computational complexity. Multiple simulation results under different traffic scenarios jointly verify the accuracy and applicability of the proposed RTPM and DRF model established from this new perspective.