Lane-Keeping for SAE Level 3 Autonomous Vehicles: A Behavior-Tree Approach Using Lidar Data for Enhanced Safety and Control

Lane-keeping is a critical function for SAE Level 3 autonomous vehicles, requiring rigorous validation and end-to-end interpretability. All recently U.S. approved level 3 vehicles are equipped with lidar, likely meant to accelerate active safety regulation compliance. Lidar offers precise and reliable surface distance and intensity measurements, enabling more effective implementation of rule-based algorithms compared to camera-based methods, which tend to rely on machine learning based methods for state-of-the-art performance. This paper presents a behavior-tree algorithm for lane-keeping using data from an Ouster OS0 lidar sensor. The algorithm evaluates factors such as road curvature, speed limits, road types (rural, urban, interstate), and the proximity of objects or humans to lane markings. It also accounts for the behavior and type of adjacent and opposing vehicles, lane occlusion, and weather conditions. The algorithm outputs safe lane keeping control instructions. The algorithm was evaluated using experimental data collected from real driving. Preliminary results demonstrate the algorithm's robustness and effectiveness in handling complex road scenarios, showing a promising future for less complex algorithms when using lidar .