Trajectory CPHD Filtering for Multiple Turning Vehicles With Decoupled Orientation and Axial-Scale Estimation

Abstract

Reliable estimation of vehicle orientation, centroid, and footprint is important for representing road-user occupancy during vehicle turns at intersections and other common road maneuvers. During a turn, the vehicle orientation and direction of motion may differ, and coupling the orientation with the axial scales can degrade both orientation and extent estimates. This paper proposes a trajectory cardinalized probability hypothesis density filter with a Decoupled Orientation and Axial-Scale Estimation (DOAM) model for multiple-vehicle tracking. The model represents the kinematic, orientation, and squared semi-axis-length states separately within each trajectory component, reducing the mutual interference between orientation variation and scale estimation without introducing an additional interacting multiple-model structure. A structured coordinate-ascent variational inference recursion jointly updates these state sequences and the measurement-source variables. The trajectory-component likelihood and weight update associated with the decoupled representation are derived while retaining the standard cardinality recursion. A fixed-lag trajectory implementation further uses current measurements to correct historical states within the smoothing window. Evaluation with signalized-intersection simulations and real onboard LiDAR measurements shows improved estimation of vehicle orientation, centroid, and extent, particularly during turns. The resulting vehicle-state and footprint estimates provide information for road-user occupancy perception and subsequent collision-risk assessment and motion planning.