# Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds

arXiv: https://arxiv.org/abs/2207.11136 · HTML: https://mlsanity.com/paper/2207.11136/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 22 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [Optimization-Based Autonomous Racing of 1:43 Scale RC Cars](https://mlsanity.com/paper/1711.07300/) (2017)
- [JuMP: A Modeling Language for Mathematical Optimization](https://mlsanity.com/paper/1508.01982/) (2015)
- [Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning](https://mlsanity.com/paper/2103.14666/) (2021)
- [HPIPM: a high-performance quadratic programming framework for model predictive control](https://mlsanity.com/paper/2003.02547/) (2020)
- [Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios](https://mlsanity.com/paper/2005.08664/) (2020)
- [Optimization-Based Hierarchical Motion Planning for Autonomous Racing](https://mlsanity.com/paper/2003.04882/) (2020)
- [Real-Time Control for Autonomous Racing Based on Viability Theory](https://mlsanity.com/paper/1701.08735/) (2017)
- [Game-Theoretic Model Predictive Control with Data-Driven Identification of Vehicle Model for Head-to-Head Autonomous Racing](https://mlsanity.com/paper/2106.04094/) (2021)
- [Autonomous Racing with AutoRally Vehicles and Differential Games](https://mlsanity.com/paper/1707.04540/) (2017)
- [A Holistic Motion Planning and Control Solution to Challenge a Professional Racecar Driver](https://mlsanity.com/paper/2103.00358/) (2021)

## Built on by (most cited first, 8 of 8)

- [A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data](https://mlsanity.com/paper/2407.16680/) (2024)
- [A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential](https://mlsanity.com/paper/2312.14808/) (2023)
- [Sampling-Based Motion Planning with Online Racing Line Generation for Autonomous Driving on Three-Dimensional Race Tracks](https://mlsanity.com/paper/2403.18643/) (2024)
- [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)
- [Fast and Modular Autonomy Software for Autonomous Racing Vehicles](https://mlsanity.com/paper/2408.15425/) (2024)
- [Towards Optimal Head-to-head Autonomous Racing with Curriculum Reinforcement Learning](https://mlsanity.com/paper/2308.13491/) (2023)
- [Reinforcement Learning-based Dynamic Adaptation for Sampling-Based Motion Planning in Agile Autonomous Driving](https://mlsanity.com/paper/2510.10567/) (2025)

## Abstract (arXiv)

This paper presents a multi-layer motion planning and control architecture for autonomous racing, capable of avoiding static obstacles, performing active overtakes, and reaching velocities above 75 m/s. The used offline global trajectory generation and the online model predictive controller are highly based on optimization and dynamic models of the vehicle, where the tires and camber effects are represented in an extended version of the basic Pacejka Magic Formula. The proposed single-track model is identified and validated using multi-body motorsport libraries which allow simulating the vehicle dynamics properly, especially useful when real experimental data are missing. The fundamental regularization terms and constraints of the controller are tuned to reduce the rate of change of the inputs while assuring an acceptable velocity and path tracking. The motion planning strategy consists of a Fren'et-Frame-based planner which considers a forecast of the opponent produced by a Kalman filter. The planner chooses the collision-free path and velocity profile to be tracked on a 3 seconds horizon to realize different goals such as following and overtaking. The proposed solution has been applied on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral accelerations up to 25 m/s2.