用数据驱动方法模拟交叉口多车行驶轨迹，提升交通仿真真实性和效率。
IntTrajSim: Trajectory Prediction for Simulating Multi-Vehicle driving at Signalized Intersections
- 基于多头自注意力机制融合信号灯信息预测车辆轨迹
- 在真实交叉口数据上验证，生成轨迹符合宏观与微观统计特征
- 提出工程导向评估指标，支持模型在仿真中闭环测试
交通仿真广泛用于评估道路基础设施的运行效率，但其基于规则的方法难以模拟真实驾驶行为。交通交叉口既是安全风险高发区（近28%致命事故、58%非致命事故发生于此），也是路网运行效率的关键节点。本文提出：能否构建一个能复现交叉口宏观与微观驾驶行为统计特性的数据驱动仿真系统？深度生成模型为建模交叉口复杂车辆动态提供了良好基础，但尚未在真实微仿真场景中验证，也缺乏交通工程相关评估指标。为此，本文设计了面向交通工程的评估指标，并构建仿真闭环测试流程。同时提出一种融合信号灯信息的多头自注意力轨迹预测模型，在评估指标上优于先前模型。
原文摘要 · Abstract (English)
Traffic simulators are widely used to study the operational efficiency of road infrastructure, but their rule-based approach limits their ability to mimic real-world driving behavior. Traffic intersections are critical components of the road infrastructure, both in terms of safety risk (nearly 28% of fatal crashes and 58% of nonfatal crashes happen at intersections) as well as the operational efficiency of a road corridor. This raises an important question: can we create a data-driven simulator that can mimic the macro- and micro-statistics of the driving behavior at a traffic intersection? Deep Generative Modeling-based trajectory prediction models provide a good starting point to model the complex dynamics of vehicles at an intersection. But they are not tested in a "live" micro-simulation scenario and are not evaluated on traffic engineering-related metrics. In this study, we propose traffic engineering-related metrics to evaluate generative trajectory prediction models and provide a simulation-in-the-loop pipeline to do so. We also provide a multi-headed self-attention-based trajectory prediction model that incorporates the signal information, which outperforms our previous models on the evaluation metrics.
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