Multi-Sensor Fusion Technology for Driving and Obstacle Avoidance

Abstract

Vehicular environment perception provides substantial support for vehicle driving and is a core component for the implementation of assisted driving and autonomous driving technologies. Constrained by physical characteristics, traditional single sensors suffer from perceptual blind spots and weak anti-interference capability, making them unsuitable for complex driving scenarios. Multi-sensor fusion technology, which integrates perceptual data from multiple types of sensors, has become the core solution to vehicular perception challenges. This paper systematically investigates the working principles, performance characteristics, and inherent limitations of commonly used sensors in vehicular environment perception. It focuses on analyzing the principles and applicability of three major multi-sensor fusion levels, compares the performance differences of mainstream fusion algorithms, explores engineering application schemes and sensor combination strategies of the technology in typical driving scenarios, and analyzes its application advantages. The results show that selecting the fusion level, algorithms, and sensor combinations according to scenario requirements can effectively improve the reliability, comprehensiveness, and real-time performance of vehicular perception. This study provides practical references for the engineering application and optimization of vehicular multi-sensor fusion technology, and is of great significance for promoting the large-scale deployment of assisted driving and autonomous driving technologies.