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PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning
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Vehicle motion planning is an essential component of autonomous driving technology. Current rule-based vehicle motion planning methods perform satisfactorily in common scenarios but struggle to generalize to long-tailed situations. Meanwhile, learning-based methods have yet to achieve superior performance over rule-based approaches in large-scale closed-loop scenarios. To address these issues, we propose PlanAgent, the first mid-to-mid planning system based on a Multi-modal Large Language Model (MLLM). MLLM is used as a cognitive agent to introduce human-like knowledge, interpretability, and common-sense reasoning into the closed-loop planning. Specifically, PlanAgent leverages the power of MLLM through three core modules. First, an Environment Transformation module constructs a Bird's Eye View (BEV) map and a lane-graph-based textual description from the environment as inputs. Second, a Reasoning Engine module introduces a hierarchical chain-of-thought from scene understanding to lateral and longitudinal motion instructions, culminating in planner code generation. Last, a Reflection module is integrated to simulate and evaluate the generated planner for reducing MLLM's uncertainty. PlanAgent is endowed with the common-sense reasoning and generalization capability of MLLM, which empowers it to effectively tackle both common and complex long-tailed scenarios. Our proposed PlanAgent is evaluated on the large-scale and challenging nuPlan benchmarks. A comprehensive set of experiments convincingly demonstrates that PlanAgent outperforms the existing state-of-the-art in the closed-loop motion planning task. Codes will be soon released.
Forward citations
Cited by 7 Pith papers
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MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs
MapTab is a new multimodal benchmark with 328 images and nearly 200k queries that shows current MLLMs have substantial difficulty with multi-criteria route planning when visual and tabular information must be combined.
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Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling
By replacing group normalization in GRPO with fixed scaling, Plan-R1 keeps safety violations dominant in the learning signal and achieves state-of-the-art reactive planning scores on nuPlan.
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Agent-driven Long-tail Simulation for Autonomous Driving
LLM agents with structured actions can drive interactive long-tail road users in nuPlan, and SemanticPlan shows current planners still fail safety and semantic completion there.
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Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities
A survey that groups graph-empowered AI agent research into planning, execution, memory, and multi-agent coordination, plus agents-for-graphs and applications.
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Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects
A survey that classifies chain-of-thought methods for autonomous driving into modular, logical, and reflective pipelines, and proposes three evolutionary stages from direct prompting to reinforcement learning.
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SD++: Enhancing Standard Definition Maps by Incorporating Road Knowledge using LLMs
SD++ enhances OpenStreetMap road centerlines by extracting lane and shoulder parameters from road manuals with LLMs and generating lane geometry algorithmically.
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A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach
A position/review paper argues data-driven model predictive control is the best route to safe, adaptive, human-like autonomous-driving motion planning, but provides no new derivation or experiment.
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