主题：Optimal Driving for Vehicle Fuel Economy under Traffic Speed Uncertainty 随机车流速度下的节能驾驶策略
举办地点：腾讯会议，ID: 589 360 599
主办单位：亚博全站yabovip888 人工智能与管理科学研究中心 科研处
Dr Hongbo Ye is a Research Assistant Professor in the Department of Electrical Engineering at Hong Kong Polytechnic University. He received BEng in Automation from University of Science and Technology of China and PhD in Civil Engineering from The Hong Kong University of Science and Technology. Previously, he worked in University of Leeds as Research Fellow and University of Liverpool as Lecturer. Dr Ye’s research includes transportation network modelling and optimisation, autonomous vehicle operation and control, and railway management. He has published papers in leading transportation journals (such as Transportation Science and Transportation Research Part B) and prestigious transportation conferences.
叶洪波博士现任香港理工大学研究助理教授。他分别于中国科学技术大学和香港科技大学获得自动化工学学士和土木工程博士学位，并曾在英国利兹大学和利物浦大学工作。叶博士的研究方向包括交通系统建模和优化，无人车控制和运营，以及铁路系统管理。他在交通领域顶级期刊（Transportation Science，Transportation Research Part B等）上发表多篇文章。
This paper uses stochastic optimization techniques to minimize the fuel consumption of a vehicle under uncertain traffic speed. Minimizing the fuel consumption of a moving vehicle can be formulated as an optimal control problem that determines the speed profile that the vehicle should follow. The fuel consumption is affected by speed, acceleration, and external parameters such as road grades and speed limits. In addition, surrounding traffic conditions, and in particular traffic speed, may prevent the vehicle from following the optimal speed profile and consequently affect the fuel economy and the journey time. Uncertainty in the traffic speed will affect the optimization of fuel-economical speed profiles, which has seldom been investigated in the literature. This paper describes stochastic optimization models with chance constraints for minimizing the fuel consumption of a vehicle traveling over a given stretch of road under a given time limit, where the traffic speed is assumed to be random and follow a certain probability distribution. Computational results are presented to evaluate the performance of the proposed models and assess the impact of traffic speed uncertainty on the desired speed profiles. The results affirm that uncertainty in traffic speeds can significantly increase the fuel consumption and the journey time of the speed profiles created by deterministic model. Such impact can be mitigated by incorporating the stochasticity at the planning stage using the stochastic optimization models described in this paper.