Head to Head

Head-to-head autonomous racing is a challenging research area focusing on developing vehicles capable of high-speed, competitive driving against opponents. Current research emphasizes robust control systems, often incorporating reinforcement learning with curriculum learning or control barrier functions to ensure both optimal performance and safety. Accurate opponent prediction models, such as Gaussian processes, are crucial for enabling safe and effective overtaking maneuvers, and these advancements have implications for broader robotics applications requiring real-time decision-making under uncertainty and high-stakes conditions. The development of accessible, scalable platforms using commercial hardware is also a key focus, facilitating wider participation in this rapidly evolving field.