多交叉口网联汽车生态驾驶策略
Multi-Intersection Connected Vehicle Eco-Driving Strategy
摘要: 信号交叉口的设置易导致车辆频繁启停，进而增加行车能耗。为缓解交通信号对电动汽车能效的负面影响并提升通行效率，本文提出一种面向多交叉口的生态驶入与离开(Multi-intersection Eco-Approach and Departure, M-EAD)算法。该算法先将复杂场景分解为基础场景单元，再设计定制化求解策略，从而显著提升计算效率。上层针对路口通行、拥堵路口及跟车三类场景分别制定优化策略：对于路口通行和拥堵路口场景，生成车辆连续通过多交叉口的可行速度区间；对于跟车场景，采用可变时距(Variable Time Headway, VTH)确定安全跟车距离。下层采用模型预测控制(Model Predictive Control, MPC)，执行速度跟踪、跟车与停车控制，在实现多目标优化的同时保障算法实时性。基于真实道路数据，搭建PreScan-CarSim-Simulation联合仿真平台。仿真结果显示，与单交叉口EAD (isolated intersection EAD, I-EAD)和基于规则的EAD (rule-based EAD, R-EAD)相比，所提M-EAD的能耗分别降低9.47%和24.15%。
Abstract: The installation of signalized intersections can lead to frequent vehicle starts and stops, increasing driving energy consumption. To mitigate the negative impact of traffic signals on the energy efficiency of electric vehicles and improve traffic flow, this paper proposes a Multi-intersection Eco-Approach and Departure (M-EAD) algorithm. This algorithm first decomposes complex scenarios into basic scenario units, then designs customized solution strategies, significantly improving computational efficiency. The upper layer formulates optimization strategies for three scenarios: intersection passage, congested intersections, and following traffic. For intersection passage and congested intersection scenarios, feasible speed ranges for vehicles to continuously pass through multiple intersections are generated; for following traffic scenarios, a Variable Time Headway (VTH) is used to determine the safe following distance. The lower layer employs Model Predictive Control (MPC) to perform speed tracking, following traffic, and stopping control, ensuring real-time performance while achieving multi-objective optimization. A PreScan-CarSim-Simulation co-simulation platform is built based on real road data. Simulation results show that, compared with isolated intersection EAD (I-EAD) and rule-based EAD (R-EAD), the proposed M-EAD reduces energy consumption by 9.47% and 24.15%, respectively.
文章引用：蔡余洪, 房开涛. 多交叉口网联汽车生态驾驶策略[J]. 人工智能与机器人研究, 2026, 15(1): 288-296. https://doi.org/10.12677/airr.2026.151028

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