基于马尔可夫链的混合动力汽车行驶工况预测研究

针对目前混合动力汽车行驶环境适应性的问题,提出一种适应于随机多变的交通状况和基于实时行驶工况预测的能量管理策略。该策略采用一阶齐次马尔可夫链状态转移概率矩阵,对主干道和快速路建立了行驶工况特征参数预测模型,并通过对预测准确度的对比分析,确定了滚动时间窗和预测视距的最佳值。研究结果表明,与传统基于既定循环工况的整车能量管理策略相比,基于工况预测的能量管理策略具有更好的燃油经济性,为新能源汽车能量管理实时控制提供了一种新方法。     

Estimation of driving style in naturalistic highway traffic using maneuver transition probabilities

Accurately estimating driving styles is crucial to designing useful driver assistance systems and vehicle control systems for autonomous driving that match how people drive. This paper presents a novel way to identify driving style not in terms of the durations or frequencies of individual maneuver states, but rather the transition patterns between them to see how they are interrelated. Driving behavior in highway traffic was categorized into 12 maneuver states, based on which 144 (12 × 12) maneuver transition probabilities were obtained. A conditional likelihood maximization method was employed to extract typical maneuver transition patterns that could represent driving style strategies, from the 144 probabilities. Random forest algorithm was adopted to classify driving styles using the selected features. Results showed that transitions concerning five maneuver states – free driving, approaching, near following, constrained left and right lane changes – could be used to classify driving style reliably. Comparisons with traditional methods were presented and discussed in detail to show that transition probabilities between maneuvers were better at predicting driving style than traditional maneuver frequencies in behavioral analysis.     

A methodology to evaluate driving efficiency for professional drivers based on a maturity model

Over the last decade, transport companies have tried to reduce fuel consumption using efficient driving programs. In them, motorists have to apply different specific techniques while driving. Thus, to succeed in this learning process there are two key elements: the knowledge of efficient driving techniques and the drivers’ motivation. The latter is a human factor which companies usually bring about by using reward systems. In this case, having a fair evaluation mechanism is the keystone to determine goal fulfilment. This paper presents a complete methodology to evaluate driving efficiency of drivers in professional fleets. The evaluation methodology is based on a continuous process which determines the maturity of the motorist in different aspects, such as the efficiency during the start of the vehicle movement, during motion or in stop events. In addition, the evaluation methodology includes an early-classification method to establish the initial efficiency level of the individual drivers which permits an adaptation of the learning process from the beginning. A dashboard has also been developed to support the evaluation methodology. 880 professional drivers have been evaluated with this methodology. Results show that the evaluation methodology identifies drivers’ weaknesses, to be improved in successive iterations of the learning process.     

Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study

Although car-following behavior is the core component of microscopic traffic simulation, intelligent transportation systems, and advanced driver assistance systems, the adequacy of the existing car-following models for Chinese drivers has not been investigated with real-world data yet. To address this gap, five representative car-following models were calibrated and evaluated for Shanghai drivers, using 2100 urban-expressway car-following periods extracted from the 161,055 km of driving data collected in the Shanghai Naturalistic Driving Study (SH-NDS). The models were calibrated for each of the 42 subject drivers, and their capabilities of predicting the drivers’ car-following behavior were evaluated.The results show that the intelligent driver model (IDM) has good transferability to model traffic situations not presented in calibration, and it performs best among the evaluated models. Compared to the Wiedemann 99 model used by VISSIM®, the IDM is easier to calibrate and demonstrates a better and more stable performance. These advantages justify its suitability for microscopic traffic simulation tools in Shanghai and likely in other regions of China. Additionally, considerable behavioral differences among different drivers were found, which demonstrates a need for archetypes of a variety of drivers to build a traffic mix in simulation. By comparing calibrated and observed values of the IDM parameters, this study found that (1) interpretable calibrated model parameters are linked with corresponding observable parameters in real world, but they are not necessarily numerically equivalent; and (2) parameters that can be measured in reality also need to be calibrated if better trajectory reproducing capability are to be achieved.     

A Bayesian Network model for contextual versus non-contextual driving behavior assessment

Driving behavior is generally considered to be one of the most important factors in crash occurrence. This paper aims to evaluate the benefits of utilizing context-relevant information in the driving behavior assessment process (i.e. contextual driving behavior assessment approach). We use a Bayesian Network (BN) model that investigates the relationships between GPS driving observations, individual driving behavior, individual driving risks, and individual crash frequency. In contrast to prior studies without context information (i.e. non-contextual approach), the data used in the BN approach is a combination of contextual features in the surrounding environment that may contribute to crash risk, such as road conditions surrounding the vehicle of interest and dynamic traffic flow information, as well as the non-contextual data such as instantaneous driving speed and the acceleration/deceleration of a vehicle. An information-aggregation mechanism is developed to aggregates massive amounts of vehicle GPS data points, kinematic events and context information into drivel-level data. With the proposed model, driving behavior risks for drivers is assessed and the relationship between contextual driving behavior and crash occurrence is established. The analysis results in the case study section show that the contextual model has significantly better performance than the non-contextual model, and that drivers who drive at a speed faster than others or much slower than the speed limit at the ramp, and with more rapid acceleration or deceleration on freeways are more likely to be involved in crash events. In addition, younger drivers, and female drivers with higher VMT are found to have higher crash risk.     

备用电源自投装置在地铁供电系统的应用

针对北京地铁八通线供电系统的特点,总结出其双路电源供电时的逻辑关系,采用备用电源自投装置来实现,提高了供电质量,又有一定的防误闭锁功能,提高了供电可靠性。     

电力机车逆变器采用8098CPU芯片实现双微机控制的研究

针对２台逆变器向异步电动机供电提出并设计了一个以８０９８ＣＰＵ为核心的双微机控制系统。在硬件设计过程中，突出了优化、灵活、可靠的基本设计思想。实验结果证明，本系统很好地实现了负载均匀分配，双机通讯、故障检测与保护等多种功能。     

自动变速器起步异常故障分析

介绍汽车自动变速器起步异常故障的类型,详细阐述起步异常故障的原因,并应用系统分析法来诊断起步异常故障。分析3例典型的起步异常故障。