# Optimization-Based Autonomous Racing of 1:43 Scale RC Cars

arXiv: https://arxiv.org/abs/1711.07300 · HTML: https://mlsanity.com/paper/1711.07300/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2017.

## Built on by (most cited first, 30 of 50)

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- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
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- [BORE: Bayesian Optimization by Density-Ratio Estimation](https://mlsanity.com/paper/2102.09009/) (2021)
- [Submodular Reinforcement Learning](https://mlsanity.com/paper/2307.13372/) (2023)
- [Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning](https://mlsanity.com/paper/2107.08325/) (2021)
- [Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach](https://mlsanity.com/paper/2302.00171/) (2023)
- [Safe Autonomous Racing via Approximate Reachability on Ego-vision](https://mlsanity.com/paper/2110.07699/) (2021)
- [Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limits](https://mlsanity.com/paper/2202.03807/) (2022)
- [Batch Bayesian optimisation via density-ratio estimation with guarantees](https://mlsanity.com/paper/2209.10715/) (2022)
- [TC-Driver: Trajectory Conditioned Driving for Robust Autonomous Racing -- A Reinforcement Learning Approach](https://mlsanity.com/paper/2205.09370/) (2022)
- [Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds](https://mlsanity.com/paper/2207.11136/) (2022)
- [Gradient-Based Trajectory Optimization With Learned Dynamics](https://mlsanity.com/paper/2204.04558/) (2022)
- [Receding Horizon Planning with Rule Hierarchies for Autonomous Vehicles](https://mlsanity.com/paper/2212.03323/) (2022)
- [DRL-Based Trajectory Tracking for Motion-Related Modules in Autonomous Driving](https://mlsanity.com/paper/2308.15991/) (2023)
- [Autonomous Driving Small-Scale Cars: A Survey of Recent Development](https://mlsanity.com/paper/2404.06229/) (2024)
- [A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data](https://mlsanity.com/paper/2407.16680/) (2024)
- [Performance-driven Constrained Optimal Auto-Tuner for MPC](https://mlsanity.com/paper/2503.07127/) (2025)
- [Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing](https://mlsanity.com/paper/2205.02953/) (2022)
- [Learning-Aided Warmstart of Model Predictive Control in Uncertain Fast-Changing Traffic](https://mlsanity.com/paper/2310.02918/) (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)
- [The Mini Wheelbot: A Testbed for Learning-based Balancing, Flips, and Articulated Driving](https://mlsanity.com/paper/2502.04582/) (2025)
- [NMPC trajectory planner for urban autonomous driving](https://mlsanity.com/paper/2105.04034/) (2021)
- [Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles](https://mlsanity.com/paper/2301.10384/) (2023)
- [er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds](https://mlsanity.com/paper/2310.18112/) (2023)
- [Action Flow Matching for Continual Robot Learning](https://mlsanity.com/paper/2504.18471/) (2025)
- [ZipMPC: Compressed Context-Dependent MPC Cost via Imitation Learning](https://mlsanity.com/paper/2507.13088/) (2025)
- [Efficient Perception, Planning, and Control Algorithm for Vision-Based Automated Vehicles](https://mlsanity.com/paper/2209.07042/) (2022)
- [A Simple Approach to Constraint-Aware Imitation Learning with Application to Autonomous Racing](https://mlsanity.com/paper/2503.07737/) (2025)

## Abstract (arXiv)

This paper describes autonomous racing of RC race cars based on mathematical optimization. Using a dynamical model of the vehicle, control inputs are computed by receding horizon based controllers, where the objective is to maximize progress on the track subject to the requirement of staying on the track and avoiding opponents. Two different control formulations are presented. The first controller employs a two-level structure, consisting of a path planner and a nonlinear model predictive controller (NMPC) for tracking. The second controller combines both tasks in one nonlinear optimization problem (NLP) following the ideas of contouring control. Linear time varying models obtained by linearization are used to build local approximations of the control NLPs in the form of convex quadratic programs (QPs) at each sampling time. The resulting QPs have a typical MPC structure and can be solved in the range of milliseconds by recent structure exploiting solvers, which is key to the real-time feasibility of the overall control scheme. Obstacle avoidance is incorporated by means of a high-level corridor planner based on dynamic programming, which generates convex constraints for the controllers according to the current position of opponents and the track layout. The control performance is investigated experimentally using 1:43 scale RC race cars, driven at speeds of more than 3 m/s and in operating regions with saturated rear tire forces (drifting). The algorithms run at 50 Hz sampling rate on embedded computing platforms, demonstrating the real-time feasibility and high performance of optimization-based approaches for autonomous racing.