Dynamic Obstacle Avoidance Technology Based on Multi-Sensor Fusion in Autonomous Driving

Abstract

With the booming development of artificial intelligence and sensor technology, various types of devices are widely used in daily life and industry. This paper reviews the current state and prospects of multi-sensor fusion technology for dynamic obstacle avoidance in autonomous systems. The purpose of this paper is to discuss the application status and future development direction of multi-sensor fusion technology in dynamic obstacle avoidance. By analyzing the current research results and technical challenges, this paper will summarize how multi-sensor fusion technology can improve the performance of dynamic obstacle avoidance systems, and discuss the application of intelligent algorithms and future research directions The limitations of traditional single-sensor approaches are demonstrated through a single-sensor versus multi-sensor comparison, emphasizing the advantages of combining data from various sensors (e.g. lidar, cameras, and IMUs) to enhance environmental perception. This fusion improves obstacle detection accuracy and system robustness, particularly in complex urban environments. Despite significant advancements, challenges remain, including data management, real-time processing, and computational efficiency.