Human Like Autonomous Driving

Human-like autonomous driving aims to create vehicles that navigate and react to complex traffic situations as humans do, prioritizing safety and smooth interaction with other road users. Current research heavily focuses on integrating large language models (LLMs) and active inference frameworks with deep learning techniques like imitation learning and model predictive control (MPC) to achieve more adaptable and human-like driving behaviors. This research is crucial for improving the safety, reliability, and acceptance of autonomous vehicles, addressing challenges in areas such as decision-making in unpredictable scenarios and ensuring compliance with social norms and legal regulations.