Connected cruise control among human-driven vehicles: Experiment-based parameter estimation and optimal control design

In this paper, we consider connected cruise control design in mixed traffic flow where most vehicles are human-driven. We first propose a sweeping least square method to estimate in real time feedback gains and driver reaction time of human-driven vehicles around the connected automated vehicle. Then we propose an optimal connected cruise controller based on the mean dynamics of human driving behavior. We test the performance of both the estimation algorithm and the connected cruise control algorithm using experimental data. We demonstrate that by combining the proposed estimation algorithm and the optimal controller, the connected automated vehicle has significantly improved performance compared to a human-driven vehicle.     

Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment

This research proposed an eco-driving system for an isolated signalized intersection under partially Connected and Automated Vehicles (CAV) environment. This system prioritizes mobility before improving fuel efficiency and optimizes the entire traffic flow by optimizing speed profiles of the connected and automated vehicles. The optimal control problem was solved using Pontryagin’s Minimum Principle. Simulation-based before and after evaluation of the proposed design was conducted. Fuel consumption benefits range from 2.02% to 58.01%. The CO₂ emissions benefits range from 1.97% to 33.26%. Throughput benefits are up to 10.80%. The variations are caused by the market penetration rate of connected and automated vehicles and v/c ratio. No adverse effect is observed. Detailed investigation reveals that benefits are significant as long as there is CAV and they grow with CAV’s market penetration rate (MPR) until they level off at about 40% MPR. This indicates that the proposed eco-driving system can be implemented with a low market penetration rate of connected and automated vehicles and could be implemented in a near future. The investigation also reveals that the proposed eco-driving system is able to smooth out the shock wave caused by signal controls and is robust over the impedance from conventional vehicles and randomness of traffic. The proposed system is fast in computation and has great potential for real-time implementation.     

Personalised assistance for fuel-efficient driving

Recent advances in technology are changing the way how everyday activities are performed. Technologies in the traffic domain provide diverse instruments of gathering and analysing data for more fuel-efficient, safe, and convenient travelling for both drivers and passengers. In this article, we propose a reference architecture for a context-aware driving assistant system. Moreover, we exemplify this architecture with a real prototype of a driving assistance system called Driving coach. This prototype collects, fuses and analyses diverse information, like digital map, weather, traffic situation, as well as vehicle information to provide drivers in-depth information regarding their previous trip along with personalised hints to improve their fuel-efficient driving in the future. The Driving coach system monitors its own performance, as well as driver feedback to correct itself to serve the driver more appropriately.     

高速列车动力系统选型设计方法研究

针对现行高速列车动力系统选型设计工作量大、过程繁琐、效率低等现状,把基于实例和规则推理的技术应用到动力系统的选型设计中.根据动力系统高压、牵引、制动模块的特点,研究了动力系统产品的结构模型,并设计了详细的选型设计流程,通过实例推理得到相似度高的实例,再根据规则推理得到准确的选型结果.在此基础上,开发了高速列车动力系统选型设计软件,以牵引电机选型设计为例,验证了该方法的有效性和可行性.     

A self-learning advanced booking model for railway arrival forecasting

Accurate short-term arrival forecasting is essential information for railway operators to conduct daily operations such as demand management strategies. Conventional time series methods apply historical arrival data which is the accumulation of reservations to project future arrivals. This study aims to utilize reservation data directly and proposes a novel advanced booking model by using the framework of case-based reasoning. The proposed model contains four modules with distinctive functions for similarity evaluation, instance selection, arrival projection, and parameter search. We have the constructed model tested on fourteen daily arrival series and compared its out-of-sample accuracy with that of four traditional benchmarks. The empirical results show that in average the proposed self-learning model may reduce at least 11% of mean square errors (MSE). Moreover, the learning scheme in the model may achieve significant reduction of MSE comparing with performance of other naïve versions.     

基于路径搜索的格值命题逻辑自动推理方法

提出一种基于路径搜索的自动推理算法．除了采取预处理外，还采用了动态的删除策略，使对大部分路径的搜索变成对一条路径搜索．可快速地完成对一类格值命题逻辑中的任何一个子句集可满足性与不可满足性的判定．文中还讨论了该算法计算的复杂性．     

Application of Case-Based Reasoning to Intelligent Support System for Coordinating Land Conflicts

To establish the institutional mechanism for land conflict coordination in China, a case-based reasoning system is developed as an intelligent support and effective manner to resolve such issues. The establishment of the case library is discussed, previous land conflict cases are archived in a structural representation format for retrieval, and the similarity algorithm is adopted to compare the case features. Group tests show a good classification performance, which reveals that the system is feasible.     

基于磁道钉导航的车辆自动驾驶模型的优化设计

介绍了基于磁道钉导航的车辆自动驾驶的原理和实现方法,分析用此方法建立仿人控制模型来实际控制车辆自动驾驶的结果,指出仿人控制模型的不足之处.然后针对上述不足引入了新的参量,做了实车实验.在实验数据的基础上,优化了车辆自动驾驶直道模型,并提出弯道模型.     

机场自动旅客输送系统规划技术及其应用

介绍了机场自动旅客输送系统的一般规划技术.规划技术涉及交通规划、运输组织、线路设计、车站布局以及辅助设施规划等内容.该技术方法已应用于上海浦东国际机场自动旅客输送系统的前期规划阶段.本项研究有助于今后国内机场自动旅客输送系统实施建设.     

人工神经网络原理及在AMT中的应用

人工神经网络是模拟人类神经系统的信息处理机制,能够有效地解决各类复杂问题。电控机械式自动变速器（AMT）以自身的优点成为自动变速器的研发热点。在介绍了人工神经网络和汽车电控机械式自动变速器的基础上,重点说明了如何将人工神经网络技术应用在汽车机械自动变速器的设计和开发中。