講座名稱：Eco-driving of Autonomous Vehicles Approaching Multiple Signalized Intersections
讲座地點：騰訊會議直播（ID:219 123 301https://meeting.tencent.com/s/qHWj29FJdisK）
孟祥宇，现任美国路易斯安那州立大学电气与计算机工程系副教授。他在加拿大阿尔伯塔大学电气与计算机工程系获得控制系统博士学位。2014年12月至2016年12月，新加坡南洋理工大学电气与电子工程学院任职Research Fellow。 2017年1月至2018年12月，美国波士顿大学系统工程系，博士后。他的研究兴趣包括多智能体系统的事件触发控制、节能多智能体覆盖控制和联网自动驾驶汽车的生态驾驶。
Connected and automated vehicles provide an intriguing opportunity for enabling users to better monitor transportation network conditions and to improve traffic flow. Their proliferation has rapidly grown, largely as a result of Vehicle-to-X technology which refers to an intelligent transportation system where all vehicles and infrastructure components are interconnected with each other. Such connectivity provides precise knowledge of the traffic situation across the entire road network, which in turn helps optimize traffic flows, enhance safety, reduce congestion, and minimize emissions. This talk will present the problem of optimally controlling trajectories of autonomous vehicles to jointly minimize travel time and energy consumption in the presence of multiple signalized intersections, which are modeled as spatiotemporal constraints on these trajectories. In addition to state and input constraints, the spatial equality and temporal inequality constraints can be viewed as interior-point constraints. This problem is addressed by first identifying the structure of the optimal acceleration profile and showing that it is characterized by several parameters subsequently used for trajectory design optimization. Therefore, the infinite dimensional optimal control problem is transformed into a finite dimensional parametric optimization problem. The simulation results show quantitatively the advantages of the proposed algorithm in terms of energy consumption and travel time.