Multi-Target Tracking Algorithm Combined with High-Precision Map

Abstract

On high-speed roads, there are certain blind areas within the radar coverage, especially when the blind zone is in curved road sections; because the radar does not have the measurement point information in multiple frames, it is easy to have a large deviation between the real trajectory and the filtered trajectory. In this paper, we propose a track prediction method combined with a high-precision map to solve the problem of scattered tracks when the targets are in the blind area. First, the lane centerline is fitted to the upstream and downstream lane edges obtained from the high-precision map in certain steps, and the off-north angle at each fitted point is obtained. Secondly, the normal trajectory is predicted according to the conventional method; for the extrapolated trajectory, the northerly angle of the lane centerline at the current position of the trajectory is obtained, the current coordinate system is converted from the north-east-up (ENU) coordinate system to the vehicle coordinate system, and the lateral velocity of the target is set to zero in the vehicle coordinate system to reduce the error caused by the lateral velocity drag of the target. Finally, the normal trajectory is updated and corrected, and the normal and extrapolated trajectories are managed and reported. The experimental results show that the accuracy and convergence effect of the proposed methods are much better than the traditional methods.

Target Tracking Algorithm Based on Adaptive Strong Tracking Extended Kalman Filter

“…The most important tasks in intelligent transportation systems include vehicle tracking estimation, which is categorized under conventional nonlinear filtering problems [15]. Thus, developing a tracking system specifically for urban roadways is quite important [16,17]. Bayesian estimation theory offers a strict framework for nonlinear filtering.…”