Autonomous driving technologies continue to evolve, yet safety validation under complex real-world conditions remains a significant hurdle. Among these technologies, the lane keeping system (LKS) plays a vital role in reducing traffic accidents caused by unintentional lane departure. Despite the widespread use of simulation environments, conventional testing methods often fall short in coverage, accuracy, and efficiency. These limitations hinder the broader deployment of autonomous systems and highlight the pressing need for a more systematic and data-driven approach to scenario design.
A recent study published in Procedia Computer Science introduces a structured framework that combines Principal Component Analysis (PCA) with intelligent optimization algorithms to enhance the scientific rigor of LKS scenario testing. The research defines testing requirements through analysis of LKS functional characteristics and operational domains, and establishes four types of representative scenario models.
The framework begins with the extraction of key discrete scene elements from a national traffic accident dataset. Using PCA, the study reduces dimensional complexity while preserving approximately 89 percent of the variance information. This reduction lowers the number of potential scenario combinations from over 6,000 to a manageable set, forming 14 lane keeping, 34 front vehicle stationary, 34 front vehicle braking, and 17 neighboring vehicle entry scenarios.
To address continuous scene variables such as speed, acceleration, curvature radius, and road adhesion coefficient, the study applies an intelligent search strategy using genetic algorithms and particle swarm optimization. These techniques identify high-risk edge cases near defined safety boundaries. The resulting optimization significantly reduces test space while maintaining scenario representativeness and improving the accuracy of performance assessments.
All proposed scenarios are validated using a hardware-in-the-loop (HIL) platform, which integrates simulation tools including MATLAB, Simulink, and VTD with real-time controllers. Through simulated driving conditions that replicate safety-critical events, the platform evaluates vehicle response metrics such as lateral acceleration, steering input, and lane deviation. Test results confirm the LKS’s effectiveness in maintaining lane control, reacting to stationary and braking vehicles, and avoiding adjacent lane intrusions.
Linfeng Hao, who contributed to this research, completed a Master’s in Data Analytics at Robert Morris University and served as a Research Graduate Assistant in the Department of Computer and Information Systems. Hao has been involved in autonomous vehicle training and development projects, including the preparation of instructional materials for AWS DeepRacer and facial recognition tracking systems. This background supports the application of simulation tools and machine learning techniques used in the study. Hao currently works at Archtec Inc., focusing on AI innovation within the automotive domain, particularly in the development of a vehicle automation service platform that advances intelligent mobility solutions.
By combining real-world traffic data, statistical modeling, and heuristic optimization with rigorous platform validation, this research offers a practical framework for evaluating lane-keeping systems in autonomous vehicles. The proposed methodology supports improved test efficiency and system reliability and contributes to the safe and scalable advancement of intelligent driving technologies.