外观
论文arXiv 2610.1750515 天前
面向网联自动驾驶车辆的碰撞感知预测性重路由框架
原文链接：A Collision-Aware Predictive Rerouting Framework for Connected and Automated Vehicles（arXiv 2610.17505 · 发布日期：2026-10-13）
A Collision-Aware Predictive Rerouting Framework for Connected and Automated Vehicles
针对城市非 recurrent 拥堵缓解，提出碰撞感知预测重路由框架，并在SUMO城市场景仿真中验证。
核心要点
- 研究通过预测与自适应路径规划缓解城市交通网络中的非 recurrent 拥堵问题。
- 框架结合SUMO微观仿真、DCRNN时空交通预测、碰撞窗口前瞻和多目标路线评分。
- 控制器依据预测拥堵、实时排放、路线长度惩罚和事件窗口约束决定是否偏离原路线。
- 仿真显示其不在总体时间、距离或排放上全面优于基线，但可显著降低重路由频率。
验证范围与局限：验证限于SUMO仿真，包含三个城市网络场景、五种CAV渗透率、三个随机种子和多种基准重路由策略。
英文摘要 (Abstract)
In this paper, we investigate the problem of mitigating non-recurrent congestion in urban transportation networks through predictive and adaptive routing for connected and automated vehicles (CAVs). We propose a collision-aware predictive rerouting framework that integrates microscopic SUMO simulation, DCRNN-based spatiotemporal traffic-state forecasting, collision-window lookahead, and multi-objective route scoring. The routing controller uses predicted congestion, live emission information, route-length penalties, and incident-window constraints to decide when CAVs deviate from their current routes. The completed simulation campaign across three urban network scenarios, five CAV penetration levels, three random seeds, and multiple benchmark rerouting strategies shows that the proposed full framework does not dominate all baselines in aggregate travel time, distance, or emissions. Instead, its principal advantage is operational stability: it maintains comparable network-level efficiency while substantially reducing rerouting frequency relative to active rerouting baselines. These results demonstrate both the promise and the tradeoffs of embedding graph-based prediction and collision awareness into real-time CAV rerouting, and they motivate further work on forecast calibration, uncertainty-aware prediction, and system-level fairness between CAV and non-CAV traffic.
作者：Tony Kinchen, Ting Bai, Andreas A. Malikopoulos
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