Unmanned Vehicle Navigation Obstacle Detection and Avoidance System Based on Deep Learning

In this paper, a system integrating high-precision sensor array, deep learning algorithm and advanced path planning technology is studied to improve the autonomous navigation ability and driving safety of unmanned vehicles. The system is divided into two core modules: obstacle detection and obstacle avoidance. In the obstacle detection module, convolutional neural network (CNN) is used for feature extraction and obstacle identification of camera images, and 3D position and size information is refined by combining with lidar data. The obstacle avoidance module is based on the model predictive control (MPC) algorithm, considering the current state of the vehicle and environmental constraints, and planning a safe and feasible driving path. In addition, the dynamic obstacle avoidance strategy dynamically adjusts the speed and direction of vehicles according to the distance, speed and direction of obstacles to ensure safe obstacle avoidance. The system has been tested in various road environments and weather conditions, showing high obstacle detection accuracy and fast obstacle avoidance response time, which proves its adaptability and stability. Aiming at the performance degradation under extreme conditions such as strong light irradiation and fog, it is improved by optimizing the deep learning model and path planning algorithm. This study is not only of theoretical value, but also of great significance to promote the practical process of unmanned vehicle technology.