An Analysis of the Impact of Time Delay of Fusion Modes for Point Clouds from Cooperative Road Vehicle Systems on Autonomous Driving

The rapid development of the new generation of communication technologies provides a foundation for cooperative perception between autonomous vehicles (AVs) and road. This advancement holds the potential to significantly enhance the perception capabilities of AVs in complex scenarios. Previous studies have explored different information fusion modes for cooperative perception, but neglected to analyze the balance between perception accuracy and communication delay. Aiming at the delay characteristics of point cloud fusion in cooperative perception of AVs, a delay impact analysis framework is proposed based on simulation, concentrating on three fusion modes: pre-fusion, feature fusion, and post-fusion. Considering the time lag of cooperative perception results caused by communication delay, the Extended Kalman Filter algorithm is used to make predictive compensation for cooperative perception results with delay. The novel metrics, namely Lag Compensation Error and equivalent time delay, are proposed for comprehensive evaluation of the impact of different fusion modes on cooperative perception results. Based on perception results from various point cloud fusion modes, a model is established to fit the relationship between average perception accuracy and translation error distribution. Utilizing the distribution characteristics of translation errors, this model serves as the basis for generating simulated trajectories with perception errors and subsequently the evaluating of cooperative perception performance. Finally, leveraging the TrajNet++ pedestrian trajectory dataset, 180 000 numerical simulations are conducted across 1 200 trajectories with various point cloud fusion modes and different delay parameters. The results demonstrate that the shorter trajectory lengths and the higher target speeds amplify the impact of delay on cooperative perception accuracy. In comparison to post-fusion with a 100 ms delay as the benchmark, the equivalent or superior cooperative perception accuracy is feasible when the feature-fusion delay is below 500 ms and the front-fusion delay is below 700 ms. In complex scenarios involving sudden target appearances or high-speed targets, it is recommended to choose low-delay, low-accuracy post-fusion modes. Conversely, it is advisable to consider feature fusion or pre-fusion modes with high delay and high accuracy. This study can provide a basis for the selection of point cloud fusion modes for cooperative perception of autonomous driving.