Source-linked AI summary
Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles
Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Ziran Wang
TL;DR
Autonomous vehicles need to interact with passengers while making sound decisions despite LLMs’ lack of direct access to physical driving environments. The paper presents a framework that uses LLMs as decision-making brains connected to vehicle sensing and actuation tools, and demonstrates context-sensitive reasoning for a safe, efficient overtaking plan.
Problem
LLMs lack direct physical-environment perception, limiting their ability to make sound decisions in current driving situations.
Method
The framework connects LLMs with perception, localization, in-cabin monitoring, and vehicle-action modules so processed environmental data informs language-based decision-making and execution.
Results
A ChatGPT-4 experiment processed vehicle, road, environmental, and driver-state data to formulate a comprehensive 9-step motion plan for safely and efficiently overtaking a front vehicle.
Takeaways & Limitations
The framework supports contextual reasoning, personalized assistance, continuous learning, and more flexible human-vehicle interaction for complex driving scenarios.
Abstract
from arXiv · showhide
The future of autonomous vehicles lies in the convergence of human-centric design and advanced AI capabilities. Autonomous vehicles of the future will not only transport passengers but also interact and adapt to their desires, making the journey comfortable, efficient, and pleasant. In this paper, we present a novel framework that leverages Large Language Models (LLMs) to enhance autonomous vehicles' decision-making processes. By integrating LLMs' natural language capabilities and contextual understanding, specialized tools usage, synergizing reasoning, and acting with various modules on autonomous vehicles, this framework aims to seamlessly integrate the advanced language and reasoning capabilities of LLMs into autonomous vehicles. The proposed framework holds the potential to revolutionize the way autonomous vehicles operate, offering personalized assistance, continuous learning, and transparent decision-making, ultimately contributing to safer and more efficient autonomous driving technologies.
1. Introduction
The paper proposes using LLMs as human-facing decision-making systems in autonomous vehicles, while pairing them with vehicle modules that provide environmental information and execute actions. This approach is intended to support natural interaction, contextual reasoning, personalization, and transparent assistance.
- Challenge: LLMs lack direct perception of the physical driving environment, limiting their ability to make sound situation-specific decisions.The paper identifies this limitation as a potential source of suboptimal or hazardous outcomes.
- Framework: The proposed framework treats LLMs as the vehicle’s decision-making “brain,” while perception, localization, and in-cabin modules provide sensory information and vehicle actions serve as its “hands.”Processed environmental data is supplied to the LLM, which produces instructions for the vehicle controller.
- Capabilities: Natural-language interaction allows drivers to communicate with vehicles through conversational commands rather than rigid instructions.The paper illustrates this with a spoken request to overtake a vehicle ahead.
- Capabilities: LLMs can combine contextual information from sources such as traffic laws and accident reports to support safety- and regulation-oriented decisions.The paper presents contextual understanding and reasoning as an advantage over autonomous vehicles without integrated LLMs.
- Capabilities: LLMs are presented as capable of zero-shot planning in unfamiliar situations, continuous personalization, and explaining decisions in simple language.These capabilities are framed as supporting adaptive assistance and trust between users and autonomous vehicles.
2. Perspective: the Role of LLMs in Advancing Autonomous Vehicles
The proposed interaction model places the LLM between driver commands, processed vehicle data, and controller actions. It uses specialized vehicle modules to supply information about the environment, location, and cabin before the LLM determines and communicates a feasible response.
- Decision-making pipeline: When a driver requests an operation, the LLM queries relevant vehicle modules for processed environmental information before making a decision.The framework combines linguistic analysis with selected sensory inputs rather than relying on the language model alone.
- Decision-making pipeline: For an overtaking request, the LLM can use vehicle speeds, distances, lane conditions, nearby traffic, and map information to assess whether the maneuver should proceed.If the request is approved, the LLM sends instructions to the controller.
- Framework architecture: The human-centric framework divides the system between a physical world containing drivers, vehicles, and traffic objects and a virtual world containing LLMs, memory, and tools.Human drivers remain central agents who send commands to the LLM while navigating roadways.
- Framework architecture: The perception module converts camera, LIDAR, and radar inputs into LLM-compatible data, while localization uses GNSS to determine the vehicle’s position.The in-cabin monitor uses internal sensors to track attention, temperature, and comfort-related conditions.
3. Review: Can LLMs Really Do This?
The review examines whether LLMs can contribute to autonomous-driving decision-making by connecting prior advances in adaptation, prompting, reasoning, memory, and sensor integration. It presents these capabilities as relevant foundations for human-centric autonomous vehicles, while the supplied passages do not report a definitive evaluation outcome.
- Review scope: The review asks whether LLMs can improve autonomous driving by actively participating in its decision-making framework.It evaluates this question through theoretical foundations, real-world implementations, current research, and use cases.
- Adaptive techniques: Parameter-efficient fine-tuning and reinforcement learning from human feedback are identified as approaches for adapting LLMs to driving tasks and human preferences.The review links these methods to legal, ethical, personalized, and trustworthy decision-making.
- Reasoning and acting: Advanced prompting methods, including chain-of-thought and external-API interaction, provide foundations for complex reasoning and tool use in autonomous vehicles.The cited approaches allow models to generate intermediate reasoning steps and decide how to interact with external APIs.
- Evidence from prior work: Prior research reports LLM capabilities in commonsense reasoning, complex decision-making, zero-shot planning, interactive conversation, and perception-action API interaction.These findings are presented as relevant to unfamiliar and dynamic autonomous-driving scenarios.
- Evidence from prior work: The review also highlights natural-language experience storage, continuous learning, and embodied models that assimilate real-world sensor data.Together, these lines of work connect historical information, adaptation, and sensory inputs with language-based decision-making.
4. Experiment: Decision-Making and Motion Planning with ChatGPT-4
The experiment uses ChatGPT-4 to process multimodal vehicle and driver information for context-sensitive overtaking decisions and motion planning. It also examines natural-language explanations, zero-shot reasoning, and memory-based personalization.
- Language interaction: The experiment showed that LLMs can explain driving decisions, module usage, and motion-planning choices in understandable language, supporting vehicle-passenger interaction and troubleshooting.
- Experiment setup: ChatGPT-4 processed perception, localization, and in-cabin monitoring data to formulate a safety-prioritized overtaking plan.The inputs included vehicle speeds and distances, road and environmental conditions, and the driver’s attention and safety measures.
- Motion planning: The LLM generated a comprehensive 9-step motion plan that efficiently executed the command to overtake the front vehicle.
- Decision-making: The system applied real-time, context-sensitive reasoning over surrounding traffic, driver attention, and traffic conditions to select an overtaking trajectory.Its decision-making adapted to the scenario rather than merely following predefined rules.
- Personalization and trust: The LLM communicated its reasoning during the overtaking command, while memory-based access to driver preferences could personalize speeds, following distances, and lane choices.The paper links this personalization to improved user satisfaction and potentially safer, more predictable driving scenarios.
- Zero-shot reasoning: The zero-shot experiment involved a complex highway overtaking maneuver with circumstances the LLM had not previously encountered, yet it generated a safe and efficient trajectory.
5. Conclusion
The conclusion presents LLM integration as a framework for making autonomous systems more flexible in complex scenarios and improving human-vehicle interaction. It emphasizes reasoning, reliability, intuitiveness, and responsiveness as the framework’s supported benefits.
- The framework integrates LLMs into autonomous vehicles to provide advanced reasoning for more flexible and responsive handling of complex real-world scenarios.
- LLMs can enrich human-vehicle interaction by providing a more reliable, intuitive, and responsive interface.
- The paper concludes that combining LLM capabilities with autonomous-vehicle modules supports human-centric autonomous driving systems.