智能网联汽车协同生态驾驶策略综述

为了跟踪近年来智能网联汽车(CAV)协同生态驾驶策略的研究进展, 分析了车辆、驾驶行为、交通网络和社会这4类因素对CAV能耗的影响程度, 以车辆、基础设施和旅行者为对象对目前CAV生态研究进行分类, 重点分析了信号交叉口生态驶入与离开、生态协同自适应巡航控制、匝道合流区生态协同驾驶、生态协同换道轨迹规划和生态路由5种典型车辆协同生态驾驶应用场景的研究现状。分析结果表明: 相比人类驾驶方式, 在任何交通流量CAV 100%渗透率的条件下和低交通流量CAV部分渗透率的条件下, CAV油耗节省效果显著, 最高可达63%, 而具有部分智能化和网联化等级的CAV油耗可至少节省7%;现有研究较少考虑人机共驾情况下, 驾驶人反应延迟和自动控制器传输延迟导致的轨迹跟踪偏离; 现有研究将车车通信/车路通信假定为理想数据交互过程, 未考虑通信拓扑、传输时延、通信失效与基站切换等因素对CAV生态协同驾驶策略的影响; 现有研究较少探讨多车道、交叉口转向-直行共用车道和U型车道等交通场景, 以及不同智能网联等级CAV与人类驾驶汽车、行人、自行车等共存的混合交通条件下的生态驾驶策略; 受限于自动驾驶技术和基础设施尚未成熟和完善, 真实交通场景下的测试验证工作尚未开展; 车辆控制、车车通信、多车协同、混合交通流场景、半实物仿真测试和真实交通场景测试等方面将是CAV协同生态驾驶策略的进一步发展方向。 

Review on connected and automated vehicles based cooperative eco-driving strategies

To track the research progress of connected and automated vehicles(CAV) based cooperative eco-driving strategies in recent years, the influences of four factors, including the vehicle, driver behavior, traffic network and social factor on the energy consumption of CAV were analyzed. The current ecological studies on CAV were classified with vehicle, infrastructure and traveler as objects. The status-quo of 5 representative types of cooperative eco-driving scenarios were emphatically analyzed, including the eco-approach and departure at the signalized intersection, eco-cooperative adaptive cruise control, eco-cooperative driving in the ramp merging area, eco-cooperative lane changing trajectory planning and eco-routing. Analysis result shows that compared with the human driving mode, CAVs can save up to 63% fuel consumption at any traffic flow with 100% penetration rate of CAV as well as in the light traffic condition with partial penetration rate of CAV. CAVs with partial automated and connected levels can save at least 7% fuel consumption. Few existing studies consider the trajectory tracking deviation caused by the driver's response delay and automatic controller transmission delay in the case of human-machine co-driving. The existing researches assume the vehicle-to-vehicle communication(V2V) and vehicle-to-infrastructure communication(V2I) as the ideal data interaction processes. The impacts of factors such as the communication topology, transmission delay, communication failure and packet loss on the CAV based cooperative eco-driving strategies are ignored. Few existing studies discuss the eco-driving strategies in these traffic scenarios, such as the multi-lanes, shared lanes for turning and through at intersection, U-turn, as well as the mixed traffic conditions of different automated and connected level CAVs coexisting with human-driven vehicles, pedestrians and bicycles. Limited by the immaturity and imperfection of automatic driving technology and infrastructure, the test and verification work in real traffic scenarios is not carried out. The vehicle control, V2V communication, multi-vehicles collaboration, mixed traffic flow scenario, hardware-in-the-loop simulation test and real traffic scenario test will be the further development direction of CAV based cooperative eco-driving strategies.