让无人车学会“社交”，具身交互智能使自动驾驶真正成为现实！

Core Viewpoint - The article discusses the development of an innovative framework for autonomous driving called "Embodied Interactive Intelligence Towards Autonomous Driving" (EIIAD), which aims to enhance the interaction and understanding between autonomous vehicles and their environment, including humans and other vehicles [3][5]. Group 1: Framework and Innovations - The EIIAD framework integrates cross-modal perception, machine learning, cognitive computing, and generative AI to create a unified method for intelligent expression and learning in the physical world [3][5]. - It establishes an end-to-end perception-cognition-behavior feedback loop, enabling autonomous vehicles to learn continuously and autonomously from fragmented driving scenarios, thereby enhancing their intelligence level [5][10]. Group 2: Interaction Models - The framework categorizes interactions into three types: vehicle-to-human, vehicle-to-vehicle, and vehicle-to-environment, each with tailored cognitive models [6][7]. - For vehicle-to-human interactions, a hypergraph neural network model is proposed to accurately predict pedestrian intentions by capturing high-level semantic relationships from multi-view spatial-temporal features [7][8]. - For vehicle-to-vehicle interactions, a deep reinforcement learning model is designed to predict joint trajectories of multiple vehicles, allowing for effective decision-making in complex scenarios [7][8]. Group 3: Real-World Application and Testing - The EIIAD framework has been validated through extensive real-world testing, with the integrated UniCVE model deployed in autonomous buses, achieving over 22,000 kilometers of safe driving and completing 45,000 navigation tasks [10][11]. - The testing revealed the model's ability to adapt and improve its predictive capabilities in high-risk scenarios through experience-based learning [10][11]. Group 4: Future Directions - Future efforts will focus on enhancing the model's perception capabilities in occluded environments, incorporating uncertainty predictions, and strengthening its memory modules to better handle complex intersection geometries [11].