- General Motors is transforming motorsport performance through advanced AI and ML technologies.
- The company applies AI in key racing series, including NASCAR, IndyCar, and sportscar racing.
- GM’s Charlotte Technical Center processes real-time data from telemetry, communications, and trackside images.
- Innovations include a real-time audio transcription system and an image analysis tool that provides instant insights.
- AI tools help optimize race strategies, manage tire wear, and assess car conditions rapidly.
- Recent advancements enabled GM’s teams to make crucial decisions, such as avoiding unnecessary pitstops, which contributed to competitive success.
Main AI News:
General Motors is making a substantial impact in the motorsports arena with its advanced deployment of artificial intelligence (AI) and machine learning (ML) technologies. While many businesses are often criticized for adopting AI due to its trendiness rather than its actual utility, GM’s approach provides compelling evidence of AI’s tangible benefits in enhancing competitive performance. The automaker’s foray into AI is particularly evident in its involvement across top motorsport series such as NASCAR, IndyCar, and sportscar racing, highlighting its commitment to leveraging technology for superior racing outcomes.
GM’s motorsport programs are focused on four key areas. NASCAR remains a central pursuit, with Chevrolet supplying engines to six teams competing in the Cup series. IndyCar, another major platform, features six teams powered by Chevy. Cadillac is also active in the IMSA’s GTP class and the World Endurance Championship’s Hypercar class, while Corvette Racing represents GM in IMSA. Jonathan Bolenbaugh, the leader of GM’s motorsports analytics team, notes that the role of AI and ML is not to replace human expertise but to support it, enhancing the capabilities of engineers and strategists.
At the heart of GM’s AI efforts is the Charlotte Technical Center, where data from various sources—including car telemetry, voice communications, text messages, and trackside photographs—are processed. This data is crucial for real-time decision-making during races. GM has developed several proprietary tools to handle this vast array of information. One such tool is a real-time audio transcription system designed to replace manual input, which improves efficiency and accuracy despite the noisy racing environment. This tool was built using a combination of open-source and proprietary code, optimized to handle the high decibel levels typical at racetracks.
Another significant advancement is GM’s image analysis tool, which processes photos from trackside photographers in record time. Previously, it took two to three minutes to get photos from a camera to the team. Now, AI integration reduces this time to just seven seconds, providing engineers with immediate insights. This rapid processing was pivotal in a recent NASCAR event where an instantaneous evaluation of a car’s condition allowed a partner team to avoid a pitstop, a decision that ultimately contributed to their playoff qualification.
In addition to immediate tactical benefits, AI at GM is used to analyze engineering aspects from images. The data gleaned from thousands of photos each race weekend helps engineers gain valuable insights into car setups, including geometry, wing angles, and ride heights. This capability transforms what would otherwise be a massive data challenge into actionable information, enhancing both competitive strategy and engineering precision.
AI’s influence extends to strategic race management as well. By analyzing real-time data and transcribed audio from race communications, GM can infer changes in track conditions and adjust strategies accordingly. For instance, if multiple drivers report similar track issues, AI models predict the likelihood of caution flags, which informs pit strategies and overall race management.
Bolenbaugh emphasizes that AI is integral to optimizing race strategy, particularly in managing tires, fuel, and lap times. The system continuously updates recommendations based on real-time data, including tire wear and race conditions. This dynamic approach ensures that strategies evolve as the race progresses, leveraging AI’s ability to process and learn from new data continuously.
Conclusion:
GM’s implementation of AI and ML technologies highlights a significant shift in motorsport strategy, setting a new benchmark for performance optimization. By leveraging these technologies, GM not only enhances real-time decision-making and operational efficiency but also gains a competitive edge in the industry. This adoption underscores the growing importance of AI in sports and could influence other teams and manufacturers to integrate similar technologies, potentially leading to broader advancements and increased investment in AI-driven solutions across the motorsports sector.
