High-Speed Trajectory Planning for Autonomous Vehicles Using a Simple Dynamic Model
For autonomous vehicle control, this work addresses the need for real-time trajectory planning that can handle near-limits driving scenarios without requiring a predefined feasible velocity reference.
The authors propose a trajectory planner for autonomous vehicles that uses a simple dynamic model derived from numerical simulations, enabling real-time high-speed trajectory planning with automatic velocity adaptation. Simulation results show the approach provides feasible, trackable trajectories and outperforms kinematic models in robustness and trajectory quality.
To improve safety and energy efficiency, autonomous vehicles are expected to drive smoothly in most situations, while maintaining their velocity below a predetermined speed limit. However, some scenarios such as low road adherence or inadequate speed limit may require vehicles to automatically adapt their velocity without external input, while nearing the limits of their dynamic capacities. Many of the existing trajectory planning approaches are incapable of making such adjustments, since they assume a feasible velocity reference is given. Moreover, near-limits trajectory planning often implies high-complexity dynamic vehicle models, making computations difficult. In this article, we use a simple dynamic model derived from numerical simulations to design a trajectory planner for high-speed driving of an autonomous vehicle based on model predictive control. Unlike existing techniques, our formulation includes the selection of a feasible velocity to track a predetermined path while avoiding obstacles. Simulation results on a highly precise vehicle model show that our approach can be used in real-time to provide feasible trajectories that can be tracked using a simple control architecture. Moreover, the use of our simplified model makes the planner more robust and yields better trajectories compared to kinematic models commonly used in trajectory planning.