Human-Centric Autonomous Systems With LLMs for User Command Reasoning
The evolution of autonomous driving has made remarkable advancements in recent years, evolving into a tangible reality. However, a human-centric large-scale adoption hinges on meeting a variety of multifaceted requirements. To ensure that the autonomous system meets the user's intent, it is essential to accurately discern and interpret user commands, especially in complex or emergency situations. To this end, we propose to leverage the reasoning capabilities of Large Language Models (LLMs) to infer system requirements from in-cabin users' commands. Through a series of experiments that include different LLM models and prompt designs, we explore the few-shot multivariate binary classification accuracy of system requirements from natural language textual commands. We confirm the general ability of LLMs to understand and reason about prompts but underline that their effectiveness is conditioned on the quality of both the LLM model and the design of appropriate sequential prompts. Code and models are public with the link \url{https://github.com/KTH-RPL/DriveCmd_LLM}.
Code (1)
Tasks
Autonomous DrivingBinary ClassificationSimilar Papers 제목 키워드 기반
Leveraging Large Language Models for Enhancing Autonomous Vehicle Perception
Autonomous vehicles (AVs) rely on sophisticated perception systems to interpret their surroundings, a cornerstone for safe navigation and decision-making. The integration of Large Language Models (LLMs) into AV perceptio…
Autonomous DrivingAutonomous VehiclesDecision MakingSensor FusionReceive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles
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 inter…
Autonomous DrivingAutonomous VehiclesDecision MakingLLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation
As personalized recommendation systems become vital in the age of information overload, traditional methods relying solely on historical user interactions often fail to fully capture the multifaceted nature of human inte…
Explainable RecommendationRecommendation SystemsEvaluating Conversational Recommender Systems with Large Language Models: A User-Centric Evaluation Framework
Conversational recommender systems (CRS) involve both recommendation and dialogue tasks, which makes their evaluation a unique challenge. Although past research has analyzed various factors that may affect user satisfact…
Recommendation SystemsLarge Language Models for Human-like Autonomous Driving: A Survey
Large Language Models (LLMs), AI models trained on massive text corpora with remarkable language understanding and generation capabilities, are transforming the field of Autonomous Driving (AD). As AD systems evolve from…
Autonomous DrivingAutonomous VehiclesDeep Reinforcement LearningSurvey