Test Scenario Design and Optimization of Automated Driving Lane Keeping System Based On PCA and Intelligent Algorithm

With the rapid development of autonomous driving technology, lane keeping system (LKS) has become an important function to improve vehicle driving safety. It effectively reduces the risk of accidents caused by lane departure by ensuring stable running of vehicles in the lane. Therefore, before large-scale application, it is particularly important to ensure its safety and reliability in complex environments. Aiming at the shortcomings of existing test methods in efficiency and scene quality, this paper proposes a test scene design and optimization method based on principal component analysis (PCA) and intelligent optimization algorithm to improve the comprehensiveness and scientificity of the test. Through the analysis of LKS system function characteristics, automatic classification and operation domain, the test requirements are defined, four types of typical test scenarios are designed, and hierarchical mathematical models are established. Based on real accident data, the key variables of discrete scene elements were extracted by PCA method to enhance scene authenticity. Aiming at the elements of the continuous scene, an intelligent optimization model based on safety boundary was constructed, which was solved by combining genetic algorithm (GA) and particle swarm optimization algorithm (PSO), and finally formed a key test scenario set covering 14 lane keeping, 34 front vehicle stationary, 34 front vehicle braking and 17 neighboring vehicles entering the scene. Through the hardware-in-the-loop test platform to verify the effectiveness of the scenario, the experimental results show that the proposed method significantly improves the test efficiency and scene quality, and provides a strong support for the full-scale LKS test and large-scale application of autonomous driving technology.