F1/10: An Open-Source Autonomous Cyber-Physical Platform
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Abstract (arXiv)
In 2005 DARPA labeled the realization of viable autonomous vehicles (AVs) a grand challenge; a short time later the idea became a moonshot that could change the automotive industry. Today, the question of safety stands between reality and solved. Given the right platform the CPS community is poised to offer unique insights. However, testing the limits of safety and performance on real vehicles is costly and hazardous. The use of such vehicles is also outside the reach of most researchers and students. In this paper, we present F1/10: an open-source, affordable, and high-performance 1/10 scale autonomous vehicle testbed. The F1/10 testbed carries a full suite of sensors, perception, planning, control, and networking software stacks that are similar to full scale solutions. We demonstrate key examples of the research enabled by the F1/10 testbed, and how the platform can be used to augment research and education in autonomous systems, making autonomy more accessible.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jan 2019.
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Citing papers per year: 2022: 4; 2023: 3; 2024: 4; 2025: 2.
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- 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racingcited by 56
- 2024A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismocited by 12
- 2022AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Enhancing Autonomous Driving Research and Educationcited by 10
- 2023Deep Dynamics: Vehicle Dynamics Modeling with a Physics-Constrained Neural Network for Autonomous Racingcited by 6
- 2025The Use of the Simplex Architecture to Enhance Safety in Deep-Learning-Powered Autonomous Systemscited by 2
- 2023Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processescited by 1
- 2024Towards Validation of Autonomous Vehicles Across Scales using an Integrated Digital Twin Frameworkcited by 1
- 2024Mixed-Reality Digital Twins: Leveraging the Physical and Virtual Worlds for Hybrid Sim2Real Transition of Multi-Agent Reinforcement Learning Policiescited by 1
- 2024Fine-Tuning Hybrid Physics-Informed Neural Networks for Vehicle Dynamics Model Estimationcited by 1
- 2022RoboMal: Malware Detection for Robot Network Systemscited by 0
- 2022An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehiclescited by 0
- 2023Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystemcited by 0
- 2025The Use of the Simplex Architecture to Enhance Safety in Deep-Learning-Powered Autonomous Systems25 Sep 2025
- 2025Learning to Drift with Individual Wheel Drive: Maneuvering Autonomous Vehicle at the Handling Limits31 Jul 2025
- 2024Fine-Tuning Hybrid Physics-Informed Neural Networks for Vehicle Dynamics Model Estimation29 Sep 2024
- 2024A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo18 Jun 2024
- 2024Mixed-Reality Digital Twins: Leveraging the Physical and Virtual Worlds for Hybrid Sim2Real Transition of Multi-Agent Reinforcement Learning Policies16 Mar 2024
- 2024Towards Validation of Autonomous Vehicles Across Scales using an Integrated Digital Twin Framework20 Feb 2024
- 2023Deep Dynamics: Vehicle Dynamics Modeling with a Physics-Constrained Neural Network for Autonomous Racing7 Dec 2023
- 2023Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem18 Sep 2023
- 2023Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes6 Jun 2023
- 2022AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Enhancing Autonomous Driving Research and Education10 Dec 2022
- 2022An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehicles3 May 2022
- 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing14 Feb 2022
1 more
- 2022RoboMal: Malware Detection for Robot Network Systems20 Jan 2022
- 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racingcited by 56
- 2022AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Enhancing Autonomous Driving Research and Educationcited by 10
- 2024Towards Validation of Autonomous Vehicles Across Scales using an Integrated Digital Twin Frameworkcited by 1
- 2023Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystemcited by 0
- 2025Learning to Drift with Individual Wheel Drive: Maneuvering Autonomous Vehicle at the Handling Limitscited by 0
- 2025Learning to Drift with Individual Wheel Drive: Maneuvering Autonomous Vehicle at the Handling Limits31 Jul 2025
- 2024Towards Validation of Autonomous Vehicles Across Scales using an Integrated Digital Twin Framework20 Feb 2024
- 2023Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem18 Sep 2023
- 2022AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Enhancing Autonomous Driving Research and Education10 Dec 2022
- 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing14 Feb 2022
- 2024A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismocited by 12
- 2023Deep Dynamics: Vehicle Dynamics Modeling with a Physics-Constrained Neural Network for Autonomous Racingcited by 6
- 2025The Use of the Simplex Architecture to Enhance Safety in Deep-Learning-Powered Autonomous Systemscited by 2
- 2023Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processescited by 1
- 2024Mixed-Reality Digital Twins: Leveraging the Physical and Virtual Worlds for Hybrid Sim2Real Transition of Multi-Agent Reinforcement Learning Policiescited by 1
- 2024Fine-Tuning Hybrid Physics-Informed Neural Networks for Vehicle Dynamics Model Estimationcited by 1
- 2022RoboMal: Malware Detection for Robot Network Systemscited by 0
- 2022An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehiclescited by 0
- 2025The Use of the Simplex Architecture to Enhance Safety in Deep-Learning-Powered Autonomous Systems25 Sep 2025
- 2024Fine-Tuning Hybrid Physics-Informed Neural Networks for Vehicle Dynamics Model Estimation29 Sep 2024
- 2024A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo18 Jun 2024
- 2024Mixed-Reality Digital Twins: Leveraging the Physical and Virtual Worlds for Hybrid Sim2Real Transition of Multi-Agent Reinforcement Learning Policies16 Mar 2024
- 2023Deep Dynamics: Vehicle Dynamics Modeling with a Physics-Constrained Neural Network for Autonomous Racing7 Dec 2023
- 2023Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes6 Jun 2023
- 2022An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehicles3 May 2022
- 2022RoboMal: Malware Detection for Robot Network Systems20 Jan 2022