Target Track Enhancement Based on Asynchronous Radar and Camera Fusion in Intelligent Driving System

In the domain of intelligent driving vehicles, radar and camera sensors play a crucial role, and their fusion has been widely acknowledged to enhance sensing range and accuracy. This article delves into the challenge of asynchronous information fusion for object detection between radar and camera, taking into consideration different sampling frequencies of radar and camera. Initially, different state observation and prediction models for radar and camera are formulated, leading to the proposal of a comprehensive target tracking model. Subsequently, the matching and fusion process of detection results is undertaken, yielding an optimal estimation as the output of the sensor information fusion system. Furthermore, the article introduces the intelligent vehicle sensor fusion task and describes the stepwise approach to sensor fusion in traffic scenarios, including coordinate transform, sensor data preprocessing, and target track. To validate the proposed sensor asynchronous fusion algorithm, a simulation experiment and a road test are conducted focusing on interaction between ego and environment vehicles. The simulation results underscore the effectiveness of the algorithm, with an average lateral error reduction of 32% and an average longitudinal error reduction of 63%. Road test shows that effective fusion can expand the detection range of sensor system and improve the detection performance.