GM Leverages AI for Competitive Edge in Motorsports

General Motors is utilizing AI and machine learning tools to gain a competitive edge in several top-level motorsports series, including NASCAR, IndyCar, IMSA, and the World Endurance Championship. The company sees these technologies as supporting human experts to make cars go faster, rather than replacing them.

Real-time audio transcription and analysis: GM has developed a proprietary tool that transcribes radio communications during races, helping teams quickly identify important information about track conditions, potential cautions, and other critical factors:

• The tool was built using a combination of open-source and proprietary code, and was trained on noisy race track environments to improve accuracy.
• By automating the transcription process, team members who would otherwise be typing out radio chatter can now focus on more valuable tasks.

Rapid image analysis for trackside photos: Another AI tool developed by GM can analyze photos taken by trackside photographers within seconds, providing valuable insights to engineers:

• The process of getting photos from the photographer’s camera to the team has been reduced from 2-3 minutes to just 7 seconds.
• The tool can quickly identify damage to cars, allowing teams to make informed decisions about whether to pit for repairs, potentially saving crucial points in the championship.
• Engineers can also use the analyzed images to glean information about competitors’ car setups, such as wing angles and ride heights, which can inform their own strategies.

Dynamic race strategy modeling: GM’s AI-powered strategy tool considers numerous factors, including lap times, fuel efficiency, tire wear, and the likelihood of cautions based on driver radio chatter, to provide real-time recommendations to teams:

• The model is constantly updated throughout the race as new data becomes available, allowing for dynamic strategy adjustments.
• Transfer learning is employed to refine the models in real-time as the race unfolds, ensuring that the recommendations remain relevant and accurate.

Broader implications for motorsports: The successful implementation of these AI and machine learning tools by GM highlights the growing importance of data-driven decision-making in motorsports:

• As the technology continues to advance, teams that effectively leverage AI and ML are likely to gain a significant competitive advantage over those that do not.
• The ability to quickly process and analyze large amounts of data from various sources can lead to better-informed strategy decisions, optimized car setups, and ultimately, improved race results.
• However, the increasing reliance on AI and ML in motorsports may also raise questions about the role of human expertise and intuition in the sport, and whether there is a risk of the technology overshadowing the skills of drivers and engineers.