Robust Cooperative Adaptive Cruise Control of Hybrid Electric Vehicles in Complex Urban Traffic Situations

在过去的几年里，合作驾驶受到了极大的关注。车载通信系统的最新进展使联网车辆车队能够开发更智能的驾驶辅助系统。协同自适应巡航控制器（CACC）是一种驾驶员辅助系统，通过无线车对车（V2V）通信提供的车间数据交换来调整车间距离。除了车辆安全性和驾驶员舒适性外，ccc还可以通过大幅减少连续走走停停的驾驶来显著提高燃油经济性，特别是在城市地区。在CACCs支持的一系列车辆中，车辆可以在更短的时间内加速或制动，从而最大限度地减少车辆之间的距离，从而优化交通效率，增强道路容量。然而，为实际应用开发可靠的ccc，特别是城市驾驶，是一个挑战。各种复杂的交通场景所产生的突发干扰，如频繁的走走停停，有时因红绿灯、十字路口、紧急停车而出现剧烈的减速和加速，以及超车、变道、切入和切断等操作，再加上V2V通信缺陷、人工驾驶汽车的意外行为等其他不确定因素，都会破坏队列的稳定性，导致交通事故的发生。******在本研究中，将开发一种具有最佳性能的鲁棒cacc的新型综合框架，以在城市驾驶中存在各种未知干扰和不确定性的情况下保持串的稳定性。我们的计划是通过以几种不同的方式执行我们设计方法的核心步骤之一来开发和评估多个健壮的cacc。在本项目结束时，我们期望确定一种具有最佳性能的健壮cacc的有效开发方法。拟议的ccc将被设计用于城市交通，并使用从车载传感器获得的关于即将到来的驾驶的更广泛的数据（例如，交通信号灯，十字路口，交通堵塞，道路施工），以更少的燃料消耗安全驾驶车辆，同时满足旅行时间和舒适标准。这些控制器将用于混合动力电动汽车（hev），这是可持续交通的近期替代方案之一。混合动力汽车有额外的电力推进，这使得它们比内燃机汽车更复杂。我们的研究结果将在一定程度上影响协作驾驶辅助系统和自动驾驶领域。设计的控制器还将为加拿大汽车公司制造的汽车增加关键的竞争价值，并使他们处于协作驾驶系统创新的前沿。这些ccc将提高混合动力汽车的安全性和燃油经济性，同时提高道路容量和交通效率。

Cooperative driving has received a great deal of attention in past years. Recent advances in vehicular communication systems enable the development of more intelligent driving assistance systems for a fleet of connected vehicles. Cooperative Adaptive Cruise Controller (CACC) is a driver assistance system for adjusting inter-vehicle distances through data exchanges between cars provided by wireless vehicle-to-vehicle (V2V) communications. In addition to vehicle safety and driver comfort, CACCs can improve significantly fuel economy by substantially reducing successive stop-and-go driving, especially in urban areas. In a string of vehicles supported by CACCs, the cars can accelerate or brake with less delays to minimize inter-vehicle distances, which leads to optimized traffic efficiency and enhanced road capacity. However, developing reliable CACCs for practical implementations, especially for urban driving, is a challenge. Sudden disturbances arising from a wide range of complex traffic scenarios, such as frequent stop-and-go driving sometimes with harsh decelerations and accelerations due to traffic lights, intersections, and emergency stops, as well as overtaking, lane-changing, cut-in and cut-out maneuvers, along with other sources of uncertainty like V2V communication defects and unexpected behavior of manually driven cars, can deteriorate the string stability of platoon and lead to car accidents. ******In this research, a novel synthesis framework for robust CACCs with optimal performance will be developed to maintain the string stability in the presence of various unknown disturbances and uncertainties in urban driving. Our plan is to develop and evaluate multiple robust CACCs by doing one of the core steps of our design methodology in several different ways. By the end of this program, we expect to identify an effective development methodology for robust CACCs with the best performance. The proposed CACCs will be designed for urban transport and use a broader range of data about upcoming driving (for instance, traffic lights, intersections, traffic jams, roadwork) obtained from on-board sensors to drive the vehicle safely with less fuel consumptions, while satisfying travel time and comfort criteria. These controllers will be developed for hybrid electric vehicles (HEVs), one of the near-term alternatives for sustainable transportations. HEVs have additional electric propulsion which makes them more complex than internal combustion engine vehicles. Our results will impact the area of cooperative driver assistance systems, and autonomous driving by some degree. The devised controllers will also add critical competitive value to the cars built by Canadian automotive companies and put them at the forefront of collaborative driving systems innovation. These CACCs will improve HEVs safety and fuel economy while enhancing road capacity and traffic efficiency.