Convex Optimization-Based Constrained Trajectory Planning for Autonomous Vehicles

This paper proposes a constrained trajectory optimization framework for autonomous vehicles (AVs) based on convex programming techniques. An enhanced kinematic vehicle model is introduced to capture dynamic motion characteristics that are often overlooked in conventional models. For obstacle avoidance, environmental constraints are transformed into convex formulations using free-space corridor methods. The trajectory planning process is further optimized through a linearized model predictive control (MPC) scheme, which considers both vehicle dynamics and environmental safety. The resulting formulation enables efficient convex optimization suitable for real-time implementation. Experimental results in various scenarios demonstrate improvements in both trajectory smoothness and safety. Furthermore, the proposed optimization method reduces the average execution time by nearly 70% compared to the nonlinear alternative, validating its computational efficiency and practical applicability.