simulating an autonomous racing car

F1Tenth autonomous vehicle

Aims: Develop software and algorithms for simulating an autonomous racing car.

Background: As autonomous vehicles (AV) technology is rapidly advancing, rigorous analysis and testing of such vehicles is particularly important to ensure their safety and performance. F1tenth is a hardware and software testbed for building an autonomous mini racing car based on a radio-controlled 1/10th-scale car and the Robot Operating System (ROS). More generally, the F1tenth platform enables AV research without the costs and risks of a real autonomous car. To facilitate development of the software/algorithmic side, F1tenth includes a fully simulated environment that replaces the physical car.
In this project, you will use the F1tenth ROS-based simulator to implement and evaluate the main algorithms underpinning an autonomous racing car. These include control, obstacle avoidance, mapping and localization, planning, and tracking.

Early Deliverables

  1. Familiarity with Robot Operating System (ROS) environment and the F1tenth simulator. Demonstrate manual control of the simulated car.
  2. Report on F1tenth car hardware, sensors and communication.
  3. Report on installation and configuration of ROS and the F1tenth simulator
  4. Implementation and evaluation of basic algorithms (PID control, wall-following, obstacle avoidance).
  5. Report(s) covering relevant theory for the implemented algorithms and experimental evaluation

Final Deliverables

  1. Implementation and evaluation of at least one localization algorithm of choice.
  2. Implementation and evaluation of at least one planning algorithm of choice.
  3. Final report, covering motivation and background around autonomous vehicles and F1tenth, description of implemented algorithms, and experimental results.

Suggested Extensions

  • Opponent pose estimation and prediction.
  • Implement and compare additional planning lgorithms.
  • Implement and evaluate raceline optimization on a seleced track.
  • Visualize future trajectories with model predictive control.
  • Learn end-to-end neural controller.


  • F1TENTH – Course Documentation https://f1tenth. -rg/learn.html
  • O’Kelly, Matthew, et al. – F1/10: An open-source autonomous cyber-physical platform. – arXiv- peprint arXiv:1901.08567 (2019)
  • O’Kelly, Matthew, et al. – F1TENTH: An Open-source Evaluation Environment for Continuous Control and Reinforcement Learning. – NeurIPS 2019 Competition and emonstration Track. PMLR, 2020

Prerequisites: Good programming skills and a willingness to install and understand new programming environm