[2505.06740] Boundary-Guided Trajectory Prediction for Road Aware and Physically Feasible Autonomous Driving

Computer Science > Robotics arXiv:2505.06740 (cs) [Submitted on 10 May 2025 (v1), last revised 4 Mar 2026 (this version, v3)] Title:Boundary-Guided Trajectory Prediction for Road Aware and Physically Feasible Autonomous Driving Authors:Ahmed Abouelazm, Mianzhi Liu, Christian Hubschneider, Yin Wu, Daniel Slieter, J. Marius Zöllner View a PDF of the paper titled Boundary-Guided Trajectory Prediction for Road Aware and Physically Feasible Autonomous Driving, by Ahmed Abouelazm and 5 other authors View PDF HTML (experimental) Abstract:Accurate prediction of surrounding road users' trajectories is essential for safe and efficient autonomous driving. While deep learning models have improved performance, challenges remain in preventing off-road predictions and ensuring kinematic feasibility. Existing methods incorporate road-awareness modules and enforce kinematic constraints but lack plausibility guarantees and often introduce trade-offs in complexity and flexibility. This paper proposes a novel framework that formulates trajectory prediction as a constrained regression guided by permissible driving directions and their boundaries. Using the agent's current state and an HD map, our approach defines the valid boundaries and ensures on-road predictions by training the network to learn superimposed paths between left and right boundary polylines. To guarantee feasibility, the model predicts acceleration profiles that determine the vehicle's travel distance along these paths while a...