Automatic obstacle avoidance of intelligent vehicles in specific environments

While autonomous obstacle avoidance technology remains a cornerstone of intelligent vehicles, performance gaps between simulations and real-world scenarios hinder practical implementation. This study investigates strategies to enhance obstacle avoidance success rates in challenging environments while narrowing the simulation-real gap. The research establishes a dynamic simulation platform utilizing CARLA and ROS tools for high-precision environmental modeling, generating a multimodal dynamic database containing 10,000 experimental datasets to improve simulation authenticity. A nonlinear risk-sensitive path planning algorithm is proposed, optimizing decision prioritization to reduce latency by 33% in high-speed scenarios and minimize collision energy prediction errors to ≤8%. Through a three-phase validation approach, vehicle sensor performance is enhanced with 40% reduced degradation in extreme environments, achieving end-to-end decision latency below 40ms. Experimental results demonstrate: 1) Simulated platform scene similarity SSIM ≥0.85; 2) Dynamic object trajectory error ≤0.2m; 3) 98% obstacle avoidance success rate in sudden scenarios. The optimized algorithm achieves pedestrian avoidance success ≥99.5%, with multimodal fusion vehicle testing demonstrating 99.2% obstacle avoidance success. This study demonstrates how coordinated optimization of simulation, algorithms, and hardware significantly enhances intelligent vehicles' obstacle avoidance capabilities in complex real-world environments, providing theoretical and technical foundations for large-scale adoption of advanced autonomous driving technologies.