TransCAVE-E：用于自适应外部人机界面的分布式虚拟现实测试平台

中文摘要 AI 辅助

外部人机界面（eHMI）正从预定义显示向适应不断变化的交通和道路使用者状态的自适应通信策略演变。这一转变需要支持人在回路（HIL）交互、软件在回路（SIL）算法执行、可复用的实验编排以及同步多模态人为因素评估的实验基础设施。本文介绍了TransCAVE-E，一个用于开发和验证自适应智能eHMI的分布式虚拟现实测试平台。该平台集成了三个耦合模块：用于可配置交通环境和实验条件的场景设计中心；用于仿真与外部算法之间双向实时耦合的eHMI算法模块；以及同步轨迹、眼动追踪、生理、系统日志和主观数据的数据管理系统。分布式多智能体架构支持行人、人类驾驶员、自动驾驶车辆和其他交通实体之间的同步交互。两个用例展示了该平台。在自动驾驶车辆（AV）-行人实验中，基于意图识别的eHMI在让行和非让行场景中分别将决策效率提高了12.8%和13.0%，在让行场景中将视线分散减少了17.1%，在非让行场景中将不必要的提示减少了40%，同时保持了交互安全性。一项人类驾驶车辆（HV）-AV研究进一步证明了在主动驾驶员交互下，博弈论信息揭示策略的实时SIL验证。TransCAVE-E为智能人车通信策略的闭环评估和迭代优化提供了可扩展且可复现的基础设施。

英文摘要

External human-machine interfaces (eHMIs) are evolving from predefined displays toward adaptive communication strategies that respond to changing traffic and road-user states. This transition requires experimental infrastructure that supports human-in-the-loop (HIL) interaction, software-in-the-loop (SIL) algorithm execution, reusable experiment orchestration, and synchronized multimodal human-factors evaluation. This paper presents TransCAVE-E, a distributed virtual-reality testbed for developing and validating adaptive and intelligent eHMIs. The platform integrates three coupled modules: a Scenario Design Center for configurable traffic environments and experimental conditions; an eHMI Algorithm Module for bidirectional real-time coupling between simulation and external algorithms; and a Data Management System that synchronizes trajectories, eye-tracking, physiological, system-log, and subjective data. A distributed multi-agent architecture supports synchronous interaction among pedestrians, human drivers, automated vehicles, and other traffic entities. Two use cases demonstrate the platform. In an AV-pedestrian experiment, an intent-recognition-based eHMI improved decision efficiency by 12.8% and 13.0% in yielding and non-yielding scenarios, reduced gaze distraction by 17.1% in the yielding scenario, and reduced unnecessary prompts by 40% in the non-yielding scenario while maintaining interaction safety. An HV-AV study further demonstrated real-time SIL validation of a game-theoretic information-disclosure strategy under active driver interaction. TransCAVE-E provides an extensible and reproducible infrastructure for closed-loop evaluation and iterative refinement of intelligent human-vehicle communication strategies.

发表机构

• University College London（伦敦大学学院）
• Ningbo University（宁波大学）
• Shanghai Maritime University（上海海事大学）
• Tongji University（同济大学）

机构由 AI 辅助整理，请以论文原文为准。