Latency Impact Analysis of Point Cloud Fusion Modes for Cooperative Perception in Autonomous Driving

Abstract

With the rapid development of communication technology, cooperative perception between vehicles and infrastructure improves the perception performance in complex scenarios. Previous studies have explored different point cloud fusion modes but ignored the cooperative analysis of perception precision and communication latency. Focusing on the problem of point cloud fusion, a latency impact analysis framework based on simulation for three typical fusion modes, former fusion, feature fusion, and postfusion, is proposed. First, the relationship between the mean average precision and distribution of translation errors of different fusion modes is described, based on which simulation trajectories are generated. The extended Kalman filter algorithm is then applied to predict and compensate for the lagged cooperative perception results. The indices lag compensation error (LCE) and equivalent latency are proposed to evaluate the final effect. Finally, numerical simulations of different point cloud fusion modes and latencies are conducted based on the TrajNet++ pedestrian trajectory dataset. The results show that the LCE is positively correlated with the latency and object speed and negatively correlated with the length of the historical trajectory and perception accuracy. Therefore, the postfusion mode with low latency and cooperative perception accuracy should be adopted for complex scenarios where the objects consistently appear suddenly and are moving fast. Conversely, the former fusion mode with high perception accuracy should be adopted. The research results provide a basis for the point cloud fusion mode selection and applicability of cooperative perception in an internet of vehicles environment.