Through the design of autonomous lane changing lateral obstacle avoidance for intelligent connected vehicles, the safety performance of vehicle autonomous driving is improved. A laser point cloud based intelligent connected vehicle autonomous lane changing lateral obstacle avoidance method is proposed, and a vehicle driving environment excitation point cloud data collection model based on an all laser radar unmanned driving system is constructed. The perception of the intelligent network vehicle driving environment is achieved through the laser radar laser point cloud image detection method. Combining the methods of obstacle detection, lane line detection, and driving area detection, this paper analyzes the correlation characteristics of obstacles and lanes for vehicle lane changing under intelligent network, searches for boundary point through the autonomous optimization method of laser point cloud until the search path reaches the threshold, and then realizes the planning and design of lateral obstacle avoidance for vehicle autonomous lane changing based on the straight line road model and the continuity principle of lane lines. The simulation results show that the proposed method achieves convergence in 5s, the obstacle coordinate identification error distance is less than 0.7, and the identification accuracy of the proposed method is more than 0.970, which improves the perception ability of lane information, accurately and efficiently estimates the driving area, and improves the obstacle avoidance ability of vehicles.