Autonomous Vehicle Control

Autonomous vehicle control aims to develop systems enabling safe and efficient self-driving capabilities, focusing on robust and adaptable control algorithms that handle diverse traffic scenarios and uncertainties. Current research emphasizes improving model accuracy and efficiency through techniques like differentiable simulation, active inference, and reinforcement learning, often integrated with model predictive control and incorporating probabilistic methods to handle uncertainty. These advancements are crucial for enhancing the safety and reliability of autonomous vehicles, impacting both the development of advanced control algorithms and the broader field of robotics and artificial intelligence.