Autonomous driving paper index
DriveAgent: Multi-Agent Structured Reasoning With LLM and Multimodal Sensor Fusion for Autonomous Driving
One-line summary
We introduce DriveAgent, a modular multi-agent autonomous driving framework that leverages large language model (LLM) reasoning combined with multimodal sensor fusion for autonomous driving.
Engineering notes
Extensive experiments demonstrate that DriveAgent substantially outperforms baseline methods, achieving a 26.31% improvement in vehicle reasoning and consistent enhancements of up to 2.85% in environmental reasoning.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
We introduce DriveAgent, a modular multi-agent autonomous driving framework that leverages large language model (LLM) reasoning combined with multimodal sensor fusion for autonomous driving. DriveAgent orchestrates specialized agents operating on camera, Light Detection and Ranging (LiDAR), Inertial Measurement Unit (IMU), and Global Positioning System (GPS) with LLM-driven analytical processes to deliver temporally aligned perception, causal reasoning, and action recommendations. The framework operates through a modular agent-based pipeline comprising four principal modules: (i) a descriptive analysis agent identifying critical sensor data events based on filtered timestamps, (ii) dedicated vehicle-level analysis conducted by LiDAR and vision agents that collaboratively assess vehicle conditions and movements, (iii) environmental reasoning and causal analysis agents explaining contextual changes and their underlying mechanisms, and (iv) an urgency-aware decision-generation agent prioritizing insights and proposing timely maneuvers. This modular design empowers the LLM to effectively coordinate specialized perception and reasoning agents, delivering cohesive, interpretable insights into complex autonomous driving scenarios. Extensive experiments demonstrate that DriveAgent substantially outperforms baseline methods, achieving a 26.31% improvement in vehicle reasoning and consistent enhancements of up to 2.85% in environmental reasoning. These results highlight the effectiveness of our LLM-driven multi-agent sensor fusion framework in boosting the robustness and reliability of autonomous driving systems.
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