Racing Scenario

Autonomous racing research focuses on developing AI agents capable of achieving human-level or superhuman performance in simulated and real-world racing environments. Current research emphasizes the development of sophisticated decision-making algorithms, including game-theoretic approaches, reinforcement learning (particularly model-based RL and adversarial imitation learning), and hierarchical control architectures that combine high-level strategic planning with low-level reactive control. These advancements are driven by the need for robust, safe, and efficient autonomous systems, with applications extending beyond racing to broader areas of robotics and autonomous driving.