F1/10: An Open-Source Autonomous Cyber-Physical Platform

Abstract

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.

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• ForzaETH Race Stack -- Scaled Autonomous Head-to-Head Racing on Fully Commercial off-the-Shelf Hardware
• RACECAR -- The Dataset for High-Speed Autonomous Racing
• Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles
• Towards Mechatronics Approach of System Design, Verification and Validation for Autonomous Vehicles
• Towards Validation of Autonomous Vehicles Across Scales using an Integrated Digital Twin Framework
• Mixed-Reality Digital Twins: Leveraging the Physical and Virtual Worlds for Hybrid Sim2Real Transition of Multi-Agent Reinforcement Learning Policies
• Scalable Supervisory Architecture for Autonomous Race Cars
• Nigel -- Mechatronic Design and Robust Sim2Real Control of an Over-Actuated Autonomous Vehicle
• Towards Sim2Real Transfer of Autonomy Algorithms using AutoDRIVE Ecosystem
• Sim-To-Real Transfer for Miniature Autonomous Car Racing
• Adaptive Lookahead Pure-Pursuit for Autonomous Racing
• Online Reachability Analysis and Space Convexification for Autonomous Racing
• Disaggregated Memory at the Edge