Reliable and Real-Time Highway Trajectory Planning via Hybrid Learning-Optimization Frameworks

--Autonomous highway driving presents a high collision risk due to fast-changing environments and limited reaction time, necessitating reliable and efficient trajectory planning. This paper proposes a hybrid trajectory planning framework that integrates the adaptability of learning-based methods with the formal safety guarantees of optimization-based approaches. The framework features a two-layer architecture: an upper layer employing a graph neural network (GNN) trained on real-world highway data to predict human-like longitudinal velocity profiles, and a lower layer utilizing path optimization formulated as a mixed-integer quadratic programming (MIQP) problem. The primary contribution is the lower-layer path optimization model, which introduces a linear approximation of discretized vehicle geometry to substantially reduce computational complexity, while enforcing strict spatiotemporal non-overlapping constraints to formally guarantee collision avoidance throughout the planning horizon. Experimental results demonstrate that the planner generates highly smooth, collision-free trajectories in complex real-world emergency scenarios, achieving success rates exceeding 97% with average planning times of 54 ms, thereby confirming real-time capability. HE trajectory planning module plays a central role in ensuring driving safety in the modern autonomous driving system. It generates an optimal continuous trajectory for autonomous vehicles (A Vs) over a future time horizon based on environmental information. This environmental information is provided by the perception module, which performs multi-sensor data fusion and feature extraction to produce real-time structured data through object detection and semantic segmentation. The control system then executes the planned trajectory by minimizing the deviation between the actual and intended vehicle behavior. Highway scenarios are constrained within structured environments characterized by high-speed operation, low-curvature roadways, and standardized traffic regulations, typically involving only rule-compliant motorized vehicles.