Research on Intelligent Obstacle Avoidance System for New Energy Vehicles Based on Multi-sensor Fusion

This paper proposes an intelligent obstacle avoidance system based on multi-sensor fusion, which is specially used for new energy vehicles. The system integrates data from multiple sensors, including lidar, ultrasonic sensors and cameras, to provide comprehensive perception of the surrounding environment. To improve the accuracy of environmental perception, this paper introduces the technology of combining Kalman filtering with motion model, which can dynamically predict and accurately perceive environmental information. This sensor fusion technology can effectively reduce the measurement error and uncertainty problems that may be caused by a single sensor, thereby improving the robustness and detection accuracy of the system. To verify the performance of the system, this paper designs and implements a set of sensor fusion algorithms, which are tested and analyzed through many simulation experiments. The experimental results show that the obstacle detection accuracy of the system under complex road conditions is improved by $12 \%$, and the path planning and obstacle avoidance efficiency are significantly optimized. In addition, the system also shows high adaptability and stability, and can cope with a variety of complex road conditions and obstacles.