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This paper addresses the advancements in on-road trajectory planning forAutonomous Passenger Vehicles (APV). Trajectory planning aims to produce aglobally optimal route for APVs, considering various factors such as vehicledynamics, constraints, and detected obstacles. Traditional techniques involve acombination of sampling methods followed by optimization algorithms, where theformer ensures global awareness and the latter refines for local optima.Notably, the Constrained Iterative Linear Quadratic Regulator (CILQR)optimization algorithm has recently emerged, adapted for APV systems,emphasizing improved safety and comfort. However, existing implementationsutilizing the vehicle bicycle kinematic model may not guarantee controllabletrajectories. We augment this model by incorporating higher-order terms,including the first and second-order derivatives of curvature and longitudinaljerk. This inclusion facilitates a richer representation in our cost andconstraint design. We also address roadway compliance, emphasizing adherence tolane boundaries and directions, which past work often overlooked. Lastly, weadopt a relaxed logarithmic barrier function to address the CILQR's dependencyon feasible initial trajectories. The proposed methodology is then validatedthrough simulation and real-world experiment driving scenes in real time.

Integrating Higher-Order Dynamics and Roadway-Compliance into Constrained ILQR-based Trajectory Planning for Autonomous Vehicles