# High-Speed Trajectory Planning for Autonomous Vehicles Using a Simple Dynamic Model

## TL;DR

This paper presents a real-time trajectory planning method for autonomous vehicles that uses a simple dynamic model within a model predictive control framework to adapt velocities near dynamic limits, improving safety and efficiency.

## Contribution

It introduces a trajectory planner based on a simplified dynamic model that can select feasible velocities and operate in real-time, outperforming kinematic models in robustness and trajectory quality.

## Key findings

- Real-time feasible trajectories generated for high-speed driving.
- Simplified dynamic model enhances robustness and trajectory quality.
- Outperforms traditional kinematic models in simulations.

## Abstract

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.