Research on traffic object recognition based on multi-sensor fusion

Abstract

The automotive advanced driver assistance systems (ADAS) are increasingly sophisticated due to the rapid advancement of intelligent connected vehicle technology. Consequently, higher demands are placed on both hardware infrastructure and onboard systems. To tackle the challenge of achieving complex traffic object recognition on limited hardware resources, a novel multi-sensor feature-level fusion strategy is proposed, which integrates data from millimeter-wave radar and visual sensors. By achieving synchronization of different sensors in both time and space domains, this strategy enhances the accuracy and robustness of traffic object recognition, while also improving computational efficiency and overall system performance.