(hu)Man vs. Machine: In the Future of Motorsport, can Autonomous Vehicles Compete?
Authors: Armand Amaritei, Amber-Lily Blackadder, Sebastian Donnelly, Lora Hernandez, James Vine, Alexander Rast, Matthias Rolf, Andrew Bradley
Organizations: Autonomous Driving and Intelligent Transport Group, Oxford Brookes University, UK · School of Engineering, Computing & Mathematics, Oxford Brookes University, UK · Artificial Intelligence, Data Analysis and Systems Institute, Oxford Brookes University, UK
Motorsport has historically driven technological innovation in the automotive industry. Autonomous racing provides a proving ground to push the limits of performance of autonomous vehicle (AV) systems. In principle, AVs could be at least as fast, if not faster, than humans. However, human driven racing provides broader audience appeal thus far, and is more strategically challenging. Both provide opportunities to push each other even further technologically, yet competitions remain separate. This paper evaluates whether the future of motorsport could encompass joint competition between humans and AVs. Analysis of the current state of the art, as well as recent competition outcomes, shows that while technical performance has reached comparable levels, there are substantial challenges in racecraft, strategy and safety that need to be overcome. Outstanding issues involved in mixed human-AI racing, ranging from an initial assessment of critical factors such as system-level latencies, to effective planning and risk guarantees are explored. The crucial non-technical aspect of audience engagement and appeal regarding the changing character of motorsport is addressed. In the wider context of motorsport and AVs, this work outlines a proposed agenda for future research to 'keep pushing the possible', in the true spirit of motorsport.
Figures & tables
Figure 1: Comparison of human and automated driven performance over the past decade based on data for the Skidpad event at Formula Student Germany
Figure 2: Comparison of timed laps along the mid-line and possible racing lines of a track with the ADS-DV (FSUK-AI).
Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA · University of Michigan Transportation Research Institute, Ann Arbor, MI 48109, USA
Department of Transport & Planning, Delft University of Technology, Delft, the Netherlands. · Department of Industrial and Systems Engineering, University of Michigan, Dearborn, Michigan, USA · 3Tandon School of Engineering, New York University, New York, United States.