Traffic Rule

Traffic rule research focuses on ensuring autonomous vehicle (AV) compliance and safety by bridging the gap between human-understandable regulations and machine-interpretable instructions. Current efforts concentrate on formalizing traffic rules using temporal logic and large language models (LLMs) to enable automated reasoning and decision-making, often integrated with reinforcement learning (RL) for safe trajectory planning. These advancements leverage various deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs like LSTMs and GRUs), and graph neural networks (GNNs), to process sensor data and predict vehicle behavior for collision avoidance and rule adherence. The ultimate goal is to develop robust and reliable AV systems that safely navigate complex traffic scenarios while strictly adhering to all applicable regulations.