# Optimization-Based Hierarchical Motion Planning for Autonomous Racing

arXiv: https://arxiv.org/abs/2003.04882 · HTML: https://mlsanity.com/paper/2003.04882/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2020.

## Built on by (most cited first, 13 of 13)

- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing](https://mlsanity.com/paper/2103.04909/) (2021)
- [A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo](https://mlsanity.com/paper/2406.12563/) (2024)
- [Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limits](https://mlsanity.com/paper/2202.03807/) (2022)
- [Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds](https://mlsanity.com/paper/2207.11136/) (2022)
- [RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms](https://mlsanity.com/paper/2501.17311/) (2025)
- [A Champion-level Vision-based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7](https://mlsanity.com/paper/2504.09021/) (2025)
- [er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds](https://mlsanity.com/paper/2310.18112/) (2023)
- [Drive Fast, Learn Faster: On-Board RL for High Performance Autonomous Racing](https://mlsanity.com/paper/2505.07321/) (2025)
- [Fast and Modular Autonomy Software for Autonomous Racing Vehicles](https://mlsanity.com/paper/2408.15425/) (2024)
- [Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing](https://mlsanity.com/paper/2604.09499/) (2026)
- [MMD-OPT : Maximum Mean Discrepancy Based Sample Efficient Collision Risk Minimization for Autonomous Driving](https://mlsanity.com/paper/2412.09121/) (2024)
- [Reinforcement Learning-based Dynamic Adaptation for Sampling-Based Motion Planning in Agile Autonomous Driving](https://mlsanity.com/paper/2510.10567/) (2025)

## Abstract (arXiv)

In this paper we propose a hierarchical controller for autonomous racing where the same vehicle model is used in a two level optimization framework for motion planning. The high-level controller computes a trajectory that minimizes the lap time, and the low-level nonlinear model predictive path following controller tracks the computed trajectory online. Following a computed optimal trajectory avoids online planning and enables fast computational times. The efficiency is further enhanced by the coupling of the two levels through a terminal constraint, computed in the high-level controller. Including this constraint in the real-time optimization level ensures that the prediction horizon can be shortened, while safety is guaranteed. This proves crucial for the experimental validation of the approach on a full size driverless race car. The vehicle in question won two international student racing competitions using the proposed framework; moreover, our hierarchical controller achieved an improvement of 20% in the lap time compared to the state of the art result achieved using a very similar car and track.