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LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning
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Although planning is a crucial component of the autonomous driving stack, researchers have yet to develop robust planning algorithms that are capable of safely handling the diverse range of possible driving scenarios. Learning-based planners suffer from overfitting and poor long-tail performance. On the other hand, rule-based planners generalize well, but might fail to handle scenarios that require complex driving maneuvers. To address these limitations, we investigate the possibility of leveraging the common-sense reasoning capabilities of Large Language Models (LLMs) such as GPT4 and Llama2 to generate plans for self-driving vehicles. In particular, we develop a novel hybrid planner that leverages a conventional rule-based planner in conjunction with an LLM-based planner. Guided by commonsense reasoning abilities of LLMs, our approach navigates complex scenarios which existing planners struggle with, produces well-reasoned outputs while also remaining grounded through working alongside the rule-based approach. Through extensive evaluation on the nuPlan benchmark, we achieve state-of-the-art performance, outperforming all existing pure learning- and rule-based methods across most metrics. Our code will be available at https://llmassist.github.io.
Forward citations
Cited by 9 Pith papers
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Who Responds When the Driver Is Gone? A Framework for Holistic Passenger Intent Understanding
Intent2Drive uses a ToM-inspired LLM reasoner on a new holistic intent dataset to infer latent passenger state and planner objectives, improving structured intent metrics while keeping competitive nuPlan closed-loop scores.
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VLMPlanner: Integrating Visual Language Models with Motion Planning
A hybrid planner that adds a vision-language model reading raw multi-view images to a real-time trajectory planner achieves modest but consistent gains on nuPlan, with an adaptive gate that cuts VLM inference frequency.
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Air-Ground Collaboration for Language-Specified Missions in Unknown Environments
A drone and a ground robot coordinated by an LLM planner over an intermittently shared semantic map complete seven natural-language missions in unknown outdoor environments.
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Doe-1: Closed-Loop Autonomous Driving with Large World Model
Doe-1 unifies perception, prediction, and planning in autonomous driving into a single autoregressive next-token generation model over image, text, and action tokens.
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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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Autoware.Flex: Human-Instructed Dynamically Reconfigurable Autonomous Driving Systems
A human-instructed driving layer that uses an LLM with a retrieval-augmented Autoware knowledge base to translate natural language into validated AutoIR configuration commands.
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Knowledge Integration Strategies in Autonomous Vehicle Prediction and Planning: A Comprehensive Survey
A survey that categorizes methods for integrating traffic rules and domain knowledge into autonomous vehicle trajectory prediction and planning.
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Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions
The paper surveys recent work, models, applications, and challenges of using LLMs in intelligent transportation systems, without presenting new experimental results.
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A Survey on Human-Centric LLMs
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