Closed Loop Autonomous Driving

Closed-loop autonomous driving focuses on creating self-driving systems that continuously monitor and adapt their behavior based on real-time sensor data and environmental interactions, aiming for safe and reliable operation. Current research emphasizes integrating diverse approaches, such as combining learning-based methods (like imitation learning and large language models) with optimization-based planners to enhance both safety and human-like driving behavior. High-fidelity simulation platforms are crucial for testing and validating these complex systems, enabling researchers to evaluate performance in diverse and challenging scenarios. This research area is vital for advancing the safety and reliability of autonomous vehicles, ultimately impacting the development of robust and trustworthy self-driving technology.