@inproceedings{yang-etal-2025-vehicleworld,
title = "{V}ehicle{W}orld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction",
author = "Yang, Jie and
Chen, Jiajun and
Yin, Zhangyue and
Chen, Shuo and
Wang, Yuxin and
Guo, Yiran and
Li, Yuan and
Zheng, Yining and
Huang, Xuanjing and
Qiu, Xipeng",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.23/",
pages = "403--442",
ISBN = "979-8-89176-335-7",
abstract = "Intelligent vehicle cockpits present unique challenges for API Agents, requiring coordination across tightly-coupled subsystems that exceed typical task environments' complexity. Traditional Function Calling (FC) approaches operate statelessly, requiring multiple exploratory calls to build environmental awareness before execution, leading to inefficiency and limited error recovery. We introduce VehicleWorld, the first comprehensive environment for the automotive domain, featuring 30 modules, 250 APIs, and 680 properties with fully executable implementations that provide real-time state information during agent execution. This environment enables precise evaluation of vehicle agent behaviors across diverse, challenging scenarios. Through systematic analysis, we discovered that direct state prediction outperforms function calling for environmental control. Building on this insight, we propose State-based Function Call (SFC), a novel approach that maintains explicit system state awareness and implements direct state transitions to achieve target conditions. Experimental results demonstrate that SFC significantly outperforms traditional FC approaches, achieving superior execution accuracy and reduced latency. We have made all implementation code publicly available on GitHub."
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<abstract>Intelligent vehicle cockpits present unique challenges for API Agents, requiring coordination across tightly-coupled subsystems that exceed typical task environments’ complexity. Traditional Function Calling (FC) approaches operate statelessly, requiring multiple exploratory calls to build environmental awareness before execution, leading to inefficiency and limited error recovery. We introduce VehicleWorld, the first comprehensive environment for the automotive domain, featuring 30 modules, 250 APIs, and 680 properties with fully executable implementations that provide real-time state information during agent execution. This environment enables precise evaluation of vehicle agent behaviors across diverse, challenging scenarios. Through systematic analysis, we discovered that direct state prediction outperforms function calling for environmental control. Building on this insight, we propose State-based Function Call (SFC), a novel approach that maintains explicit system state awareness and implements direct state transitions to achieve target conditions. Experimental results demonstrate that SFC significantly outperforms traditional FC approaches, achieving superior execution accuracy and reduced latency. We have made all implementation code publicly available on GitHub.</abstract>
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%0 Conference Proceedings
%T VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction
%A Yang, Jie
%A Chen, Jiajun
%A Yin, Zhangyue
%A Chen, Shuo
%A Wang, Yuxin
%A Guo, Yiran
%A Li, Yuan
%A Zheng, Yining
%A Huang, Xuanjing
%A Qiu, Xipeng
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F yang-etal-2025-vehicleworld
%X Intelligent vehicle cockpits present unique challenges for API Agents, requiring coordination across tightly-coupled subsystems that exceed typical task environments’ complexity. Traditional Function Calling (FC) approaches operate statelessly, requiring multiple exploratory calls to build environmental awareness before execution, leading to inefficiency and limited error recovery. We introduce VehicleWorld, the first comprehensive environment for the automotive domain, featuring 30 modules, 250 APIs, and 680 properties with fully executable implementations that provide real-time state information during agent execution. This environment enables precise evaluation of vehicle agent behaviors across diverse, challenging scenarios. Through systematic analysis, we discovered that direct state prediction outperforms function calling for environmental control. Building on this insight, we propose State-based Function Call (SFC), a novel approach that maintains explicit system state awareness and implements direct state transitions to achieve target conditions. Experimental results demonstrate that SFC significantly outperforms traditional FC approaches, achieving superior execution accuracy and reduced latency. We have made all implementation code publicly available on GitHub.
%U https://aclanthology.org/2025.findings-emnlp.23/
%P 403-442
Markdown (Informal)
[VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction](https://aclanthology.org/2025.findings-emnlp.23/) (Yang et al., Findings 2025)
ACL
- Jie Yang, Jiajun Chen, Zhangyue Yin, Shuo Chen, Yuxin Wang, Yiran Guo, Yuan Li, Yining Zheng, Xuanjing Huang, and Xipeng Qiu. 2025. VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 403–442, Suzhou, China. Association for Computational Linguistics.