用轨迹预测与图神经网络，自动识别路口危险区，提升自动驾驶安全
Roundabout Dilemma Zone Data Mining and Forecasting with Trajectory Prediction and Graph Neural Networks
- 构建基于图神经网络的多智能体轨迹预测模型，融合动态行为与语义地图
- 在真实数据上实现高精度预测，误报率低至0.1
- 适用于自动驾驶与交通管理，尤其适合复杂环岛场景
交通环岛作为复杂且关键的道路场景，对自动驾驶车辆构成重大安全挑战。特别是车辆在环岛交叉口进入犹豫区（DZ）的情况尤为关键。本文提出一种自动化系统，利用轨迹预测技术，专门针对环岛交叉口的DZ事件进行预测。该系统旨在提升自动驾驶与人工驾驶的安全标准。其核心是一个模块化、图结构的递归模型，能够预测各类交通参与者的行为轨迹，综合考虑个体动态特性及异构数据（如语义地图）。该模型基于图神经网络，有助于预测DZ事件并增强交通管理决策能力。我们使用真实世界环岛交叉口数据集对该系统进行了评估。实验结果表明，本研究提出的犹豫区预测系统实现了高精度，误报率仅为0.1。此项研究推进了环岛DZ数据挖掘与预测的发展，为自动驾驶时代的交叉口安全提供了保障。
原文摘要 · Abstract (English)
Traffic roundabouts, as complex and critical road scenarios, pose significant safety challenges for autonomous vehicles. In particular, the encounter of a vehicle with a dilemma zone (DZ) at a roundabout intersection is a pivotal concern. This paper presents an automated system that leverages trajectory forecasting to predict DZ events, specifically at traffic roundabouts. Our system aims to enhance safety standards in both autonomous and manual transportation. The core of our approach is a modular, graph-structured recurrent model that forecasts the trajectories of diverse agents, taking into account agent dynamics and integrating heterogeneous data, such as semantic maps. This model, based on graph neural networks, aids in predicting DZ events and enhances traffic management decision-making. We evaluated our system using a real-world dataset of traffic roundabout intersections. Our experimental results demonstrate that our dilemma forecasting system achieves a high precision with a low false positive rate of 0.1. This research represents an advancement in roundabout DZ data mining and forecasting, contributing to the assurance of intersection safety in the era of autonomous vehicles.
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