PaperlayerView exact revision on arXiv

Source-linked AI summary

Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles

Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Ziran Wang

arXiv:2310.08034v1cs.HCcs.AIcs.RO

TL;DR

Autonomous vehicles need richer interaction and adaptation than conventional transportation systems provide. The paper proposes an LLM-centered framework connected to perception, localization, monitoring, memory, and control tools, and reports improved decision-making with chain-of-thought prompting alongside personalized driving through verbal feedback. The paper also identifies fine-tuning, benchmarking, real-time efficiency, and ethical implications as open directions.

  • Problem

    The paper addresses how LLM language and reasoning capabilities can be integrated into autonomous vehicles to support human-centric interaction, personalization, and decision-making.

  • Method

    The framework places LLMs at the center of autonomous-vehicle decision-making and connects them with specialized tools, memory, driver commands, and vehicle control.

  • Results

    Experiments in driving scenarios indicate that chain-of-thought prompting improves driving decisions, while verbal feedback can influence driving behavior and support personalization.

  • Takeaways & Limitations

    LLM integration offers a pathway toward user-centric autonomous vehicles with more transparent interaction, adaptive support, and personalized driving experiences.

  • Takeaways & Limitations

    The paper leaves fine-tuning for vehicle-specific tasks, standardized benchmarking, real-time decision-making efficiency, and ethical and social implications for future work.

Abstract

from arXiv · show

The fusion of human-centric design and artificial intelligence (AI) capabilities has opened up new possibilities for next-generation autonomous vehicles that go beyond transportation. These vehicles can dynamically interact with passengers and adapt to their preferences. This paper proposes a novel framework that leverages Large Language Models (LLMs) to enhance the decision-making process in autonomous vehicles. By utilizing LLMs' linguistic and contextual understanding abilities with specialized tools, we aim to integrate the language and reasoning capabilities of LLMs into autonomous vehicles. Our research includes experiments in HighwayEnv, a collection of environments for autonomous driving and tactical decision-making tasks, to explore LLMs' interpretation, interaction, and reasoning in various scenarios. We also examine real-time personalization, demonstrating how LLMs can influence driving behaviors based on verbal commands. Our empirical results highlight the substantial advantages of utilizing chain-of-thought prompting, leading to improved driving decisions, and showing the potential for LLMs to enhance personalized driving experiences through ongoing verbal feedback. The proposed framework aims to transform autonomous vehicle operations, offering personalized support, transparent decision-making, and continuous learning to enhance safety and effectiveness. We achieve user-centric, transparent, and adaptive autonomous driving ecosystems supported by the integration of LLMs into autonomous vehicles.

I. INTRODUCTION

The paper motivates integrating LLMs into autonomous vehicles to support natural interaction, contextual reasoning, personalization, and adaptive decision-making. Its framework combines LLM reasoning with vehicle tools that provide environmental information and execute decisions.

  • I. INTRODUCTION: A spoken request such as “Pass the vehicle in front of me” can prompt the LLM to assess conditions and recommend or execute a safe maneuver.The example illustrates the intended connection between verbal commands, real-time safety assessment, and vehicle control.
  • I. INTRODUCTION: The proposed integration is intended to make autonomous vehicles more adaptive to complex road interactions while improving safety, efficiency, and user experience.The paper presents these as potential benefits of combining LLM knowledge and reasoning with autonomous-driving systems.
  • I. INTRODUCTION: LLMs can support natural-language interaction, contextual understanding, zero-shot planning, and continuous personalization in autonomous vehicles.These capabilities are presented as advantages spanning communication, reasoning, unfamiliar situations, and adaptation to driver preferences.
  • I. INTRODUCTION: The framework positions LLMs as a decision-making brain while perception, localization, and in-cabin modules provide sensory information and the controller executes instructions.This arrangement addresses the LLMs’ lack of direct physical-world perception by connecting them to specialized vehicle tools.

B. Organization

The paper organizes its review around the feasibility of LLMs for human-centric autonomous driving and surveys adaptation, alignment, and prompting techniques relevant to this goal.

  • B. Organization: Section II reviews theoretical foundations and real-world implementations to assess whether LLMs can participate in autonomous-driving decision-making.The review considers use cases and potential challenges in applying LLMs to human-centric autonomous driving.
  • B. Organization: Parameter-efficient fine-tuning adapts pretrained LLMs to specialized downstream applications while reducing the number of trainable parameters or using learned soft prompts.The paper highlights low-rank decomposition and prompt tuning as relevant techniques for autonomous-driving tasks.
  • B. Organization: RLHF aligns LLM systems more closely with human preferences through human-in-the-loop instruction following and alternative methods requiring minimal human input.The cited approaches are presented as strategies for controlling model behavior.
  • B. Organization: Chain-of-thought and related prompting methods support complex reasoning, intermediate program generation, and interaction with external APIs.These techniques are identified as useful for LLM-based autonomous-driving applications.

B. Advancements in LLMs: Implications for Autonomous Driving Decision-Making

The reviewed research presents LLMs as promising components for autonomous-driving decision-making because they can reason about common-sense, novel, interactive, and sensor-grounded situations. Prior work also indicates that fine-tuning and continuous learning may improve adaptation over time.

  • B. Advancements in LLMs: Implications for Autonomous Driving Decision-Making: Recent research reports that LLMs perform well on many commonsense tasks, motivating their investigation for autonomous-driving decisions.The paper connects commonsense performance with the potential to make feasible decisions in driving scenarios.
  • B. Advancements in LLMs: Implications for Autonomous Driving Decision-Making: PaLM demonstrates that LLMs can tackle complex reasoning tasks and exceed human performance, suggesting relevance to complex autonomous-driving judgments.The paper presents this finding as evidence of LLM reasoning capacity in driving-related decision-making research.
  • B. Advancements in LLMs: Implications for Autonomous Driving Decision-Making: Zero-shot reasoning enables LLMs to handle novel and unfamiliar situations, while fine-tuning can enhance performance when training data are limited.These capabilities are relevant to vehicles operating in dynamic environments and data-constrained tasks.
  • B. Advancements in LLMs: Implications for Autonomous Driving Decision-Making: Research in transportation and robotics shows LLM potential for zero-shot planning, interactive conversations, and interaction with perception-action APIs.These capabilities align with autonomous vehicles’ requirements for planning and communication with external modules.
  • B. Advancements in LLMs: Implications for Autonomous Driving Decision-Making: Continuous learning could help LLMs adapt to changing road conditions and improve performance over time.Embodied language models additionally point toward linking real-world sensor data with language-based understanding and decisions.

III. PERSPECTIVE: THE ROLE OF LLMS IN ADVANCING AUTONOMOUS VEHICLES

The proposed framework connects driver commands and vehicle sensing to LLM-based reasoning, then routes feasible and safe decisions to the controller. It combines physical-world components with virtual tools, memory, and driver preferences to support adaptive operation.

  • III. PERSPECTIVE: THE ROLE OF LLMS IN ADVANCING AUTONOMOUS VEHICLES: When a driver requests an operation, the LLM queries relevant modules, combines processed sensory information with the command, and sends safe instructions to the controller.The pipeline links language analysis, module data, feasibility and safety assessment, and actuation.
  • III. PERSPECTIVE: THE ROLE OF LLMS IN ADVANCING AUTONOMOUS VEHICLES: For overtaking, the LLM can request target-vehicle distance and speed, ego-vehicle velocity, lane conditions, and nearby-vehicle information before deciding whether to execute.The example specifies the environmental information used for the maneuver decision.
  • III. PERSPECTIVE: THE ROLE OF LLMS IN ADVANCING AUTONOMOUS VEHICLES: The framework separates a physical world of drivers, vehicles, and traffic objects from a virtual world containing LLMs, memory, and vehicle tools.Drivers issue commands while the virtual components process information and support decisions.
  • III. PERSPECTIVE: THE ROLE OF LLMS IN ADVANCING AUTONOMOUS VEHICLES: Memory stores past operations, driver preferences, maps, and local laws so the LLM can reuse experience and adapt decisions across scenarios.This repository is intended to support continuous learning and more context-sensitive decisions.

IV. EXPERIMENTS: CASES STUDY WITH GPT-4

The experiments use GPT-4 in closed-loop HighwayEnv scenarios to evaluate interpretation, reasoning, environmental interaction, and personalization. They compare standard prompting with chain-of-thought prompting across highway decision tasks.

  • Experimental Setup: GPT-4 was evaluated in HighwayEnv for interpretation, chain-of-thought reasoning, personalization, and interaction with environmental conditions.Tools and memory modules supplied environmental information that the LLM could not access directly.
  • Experimental Setup: The study compared prompting with environmental observations and solutions against prompting that additionally included a detailed decision-making process.The second setup was intended to assess improvements in performance, adaptability, and interpretation from in-context learning and chain-of-thought prompting.
  • Simulation Implementation Details: The simulation used a structured action set, including lane changes and speed adjustments, while Lane Keeping Assist remained continuously active.Lane identification counted from the rightmost lane, and the action set was designed to test varied highway scenarios.
  • Highway Scenarios: The highway experiments tested safe and unsafe overtaking scenarios to examine how LLMs react and decide under differing conditions.The scenarios were visualized in Figs. 3 and 4, with two prompting methods applied to each.
  • Highway Scenarios: Chain-of-thought prompting enabled the vehicle to assess overtaking safety, whereas standard prompting produced reckless choices that often resulted in accidents.The reported contrast concerns both safe and unsafe overtaking scenarios.

C. The Merging Senario

The merging experiments tested whether verbal commands could modulate autonomous driving style in real time. Conservative commands produced more safety-oriented behavior, while aggressive commands produced faster but more risk-prone behavior.

  • Merging Scenario: Verbal feedback enabled the LLMs to adjust driving behavior toward individual preferences during autonomous driving.The paper frames this adjustment as a route toward personalized driving experiences.
  • Merging Scenario: The experiment compared “Drive conservatively,” “Drive aggressively,” and no-query conditions to assess command-sensitive behavior during highway driving.The no-query condition represented default vehicle behavior without explicit instructions.
  • Merging Scenario: Conservative instructions yielded lower acceleration, less aggressive steering, lower top speed, greater front gap, and longer route-completion time.These changes were characterized as a more safety-oriented driving approach.
  • Merging Scenario: Aggressive instructions yielded higher acceleration, more pronounced steering inputs, increased top speed, smaller front gap, and reduced route-completion time.The resulting behavior was described as more time-efficient but risk-prone.
  • Merging Scenario: Without an explicit command, the LLMs exhibited driving behavior between the conservative and aggressive conditions.Across the conditions, the experiments demonstrated dynamic adjustment based on real-time user feedback.

V. ANALYSIS AND DISCUSSION

The analysis reports that in-context learning improved LLM behavior in highway overtaking and merging by supplying scenario-specific guidance. It also presents this guidance as an adaptable alternative to repeated retraining for new situations.

  • In-Context Learning: Scenario-specific examples and operational thoughts enabled LLMs to navigate highway overtaking and merging challenges successfully.The experiments identify context as important for aligning generalized LLM knowledge with autonomous-driving scenarios.
  • In-Context Learning: Providing scenario-specific contexts improved predictive performance and directed LLM behavior toward desired outcomes.The reported improvement concerns prompts tailored to autonomous-driving situations.
  • In-Context Learning: Additional context helped LLMs navigate intricate scenarios and avoid crashes observed when no context was provided.The passage contrasts contextual guidance with traditional models that may require retraining or a pretrained model for new scenarios.
  • In-Context Learning: In-context learning recalibrated decision-making through relevant guidance, reducing the computational and financial overhead associated with constantly retraining traditional models.The paper presents this as a more cost-effective alternative in its discussion of adaptability.

B. Reasoning

The reasoning framework combines multimodal vehicle information with context-sensitive LLM reasoning to formulate safe and efficient driving plans. The experiments also identify contextual input as a condition for reliable performance and describe chain-of-thought prompting as a guiding structure.

  • Reasoning: LLMs processed vehicle speeds, distances, road conditions, and in-cabin safety information to formulate a motion plan for overtaking.The plan prioritized safety while executing the driver’s command.
  • Reasoning: The LLM evaluated surrounding traffic and vehicle conditions to determine a safe and efficient overtaking trajectory in real time.The reported reasoning adapted to circumstances rather than merely following predefined rules.
  • Reasoning: Without an initial framework or examples, LLMs were prone to suboptimal actions that caused vehicle crashes in highway overpassing and merging experiments.The analysis identifies dependence on contextual input as a central performance boundary.
  • Chain-of-Thoughts: Chain-of-thought prompting supplied structured reasoning and acted as a guiding signal aligned with human-like reasoning and practical driving considerations.The paper suggests that repeated exposure could help generalize these guidelines across similar scenarios.
  • Chain-of-Thoughts: Adding contextual structure improved reliability and feasibility in intricate scenarios such as highway overpassing and merging.The paper also frames safety constraints and legal guidelines as preventing hazardous behavior and supporting road-law compliance.
  • Chain-of-Thoughts: The chain-of-thought framework is presented as supporting personalized driving by adjusting behavior to individual preferences for style, comfort, and responsiveness.The passage describes personalization as a potential application of the framework.

D. Personalization

LLMs translate verbal instructions into distinct driving strategies, enabling continuous personalization from conservative to aggressive behavior. The framework also envisions learning individual preferences over time and extending personalization beyond driving.

  • D. Personalization: Verbal commands produce distinct driving strategies, including conservative, aggressive, and no-instruction behaviors.The experiments report varying driving statistics across these instruction conditions.
  • D. Personalization: Continuous verbal feedback can adjust driving behavior across conservative and aggressive modes for a personalized driving experience.The paper describes this adaptation as occurring in response to real-time driver input.
  • D. Personalization: A feedback loop could allow LLMs to learn and anticipate individual preferences by developing a profile for each user.The envisioned profile would support preference-aware behavior even before new instructions are provided.
  • D. Personalization: Personalization could extend beyond driving behavior to in-vehicle environment controls and route preferences.

E. Interpretation

LLM-based autonomous driving can make complex decisions understandable through explicit reasoning, but its performance remains bounded by contextual input, benchmarking gaps, real-time robustness, and unresolved ethical questions.

  • E. Interpretation: LLMs can explain complex driving decisions by articulating the factors and reasoning behind actions such as overtaking.The paper links these explanations with enhanced transparency and greater occupant confidence.
  • E. Interpretation: Without an initial framework or examples, LLMs may take suboptimal actions that cause crashes in highway and merging experiments.
  • E. Interpretation: A standardized benchmark is needed to enable objective comparisons of LLM capabilities across autonomous-driving tasks.The proposed scope includes voice-command recognition and data-driven motion planning with quantitative and qualitative metrics.
  • E. Interpretation: More research is needed to make LLM real-time processing more efficient and robust in real-world applications.
  • E. Interpretation: Ethical and social questions concerning accountability and data privacy remain open as LLMs receive greater responsibility for driving decisions.

B. Conclusion

The paper presents LLM integration as a framework for making autonomous vehicles more flexible, responsive, and capable of richer human-car interaction. Experiments verify utility across multiple vehicle modules while identifying important directions for further development.

  • B. Conclusion: The framework uses LLMs to support flexible responses to complex real-world driving scenarios and richer human-car interaction.It contrasts LLM-based reasoning and natural-language capabilities with static decision-making and limited traditional interfaces.
  • B. Conclusion: Experiments verified the utility of LLM integration across decision-making, motion planning, and human-vehicle communication.
  • B. Conclusion: The findings point toward autonomous vehicles combining efficiency, safety, and user-centric design through LLM integration.
Loading 2310.08034v1…