Development of advanced driver assistance systems with vehicle hardware-in-the-loop simulations

This paper presents a new method for the design and validation of advanced driver assistance systems (ADASs). With vehicle hardware-in-the-loop (VEHIL) simulations, the development process, and more specifically the validation phase, of intelligent vehicles is carried out safer, cheaper, and is more manageable. In the VEHIL laboratory, a full-scale ADAS-equipped vehicle is set up in a hardware-in-the-loop simulation environment, where a chassis dynamometer is used to emulate the road interaction and robot vehicles to represent other traffic. In this controlled environment, the performance and dependability of an ADAS is tested to great accuracy and reliability. The working principle and the added value of VEHIL are demonstrated with test results of an adaptive cruise control and a forward collision warning system. On the basis of the ‘V’ diagram, the position of VEHIL in the development process of ADASs is illustrated.     

桑塔纳2000型轿车AFE发动机Motronic控制系统的结构与故障诊断

介绍了桑塔纳２０００型轿车ＡＦＥ发动机Ｍｏｔｒｏｎｉｃ控制系统的组成，结构特点及故障诊断，该系统采 微型计算机，能对发动机运行工况参数的输入信号进行与数据处理，它与单纯电控制汽油喷系统相比具有许多优点。     

基于PCC技术的机车柴油机起动控制

贝加莱公司生产的B＆R2000系列PCC是一种可实现从简单的逻辑控制到复杂分散化的自动控制系统 以东风4C机车为例,分析了目前内燃机车柴油机起动中存在的蓄电池亏能及燃烧状态不好等问题,根据串励电动机及柴油机起动时的转速变化规律,提出了基于PCC的柴油机起动及过程控制,分析了系统的工作原理及硬件构成     

汽车智能化技术分析（Ⅰ）

现代汽车利用各种通信、网络、自动化等信息技术手段为人们提供各种舒适、安全、便捷、智能的服务,以实现更安全、更舒适、更便捷的汽车生活,这种技术称为智能化技术。本文通过对全球市场车型进行调查,总结出目前在售车型的所有先进智能化技术,并对各种技术进行逐一简述,分类归纳。     

路企直通运输在大窑湾港的运用

采用传统的铁路运输组织方法,铁路和港口间存在交接车手续复杂,机车等待时间长,路港信息交流不畅等问题.本文论述了路企直通运输方式在大窑湾港的运用,重点研究了运输组织流程再造、统一调度指挥、合署办公机制等优化措施.直通运输的实施,提高了港口和港前站的作业效率,实现了铁路和港口的双赢.     

港口长距离大运量带式输送机驱动方式探讨

长距离高带速大功率的带式输送机能够有效地节省运输成本,给企业带来显著的经济社会效益,因此在各行各业中的应用越来越多。港口物流运输具有其自身的生产作业特点,对带式输送机的驱动有特殊的要求,软启动驱动不但能够节约能耗,而且能够满足港口带式输送机的启动性能要求,已经越来越多地应用于港口物流企业。     

Lane changing intention recognition based on speech recognition models

Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage.     

驾驶辅助技术在事故预防中的应用效果

越来越多的车型都搭载了驾驶辅助系统,部分驾驶人对这类系统的实际效果持怀疑态度。本文通过调研国内外相关研究,对几类主流驾驶辅助系统的事故预防效果进行综述。与相同条件下未安装驾驶辅助系统的车辆对比,研究人员发现多数驾驶辅助系统在事故预防方面发挥了明显的作用,降低了相关的事故率、保险索赔率,其中前向和后向的自动紧急制动系统效果最为显著。此外,驾驶人对驾驶辅助系统的接受度以及响应度均会影响事故预防效果。     

Real time detection of driver attention: Emerging solutions based on robust iconic classifiers and dictionary of poses

Real time monitoring of driver attention by computer vision techniques is a key issue in the development of advanced driver assistance systems. While past work mostly focused on structured feature-based approaches, characterized by high computational requirements, emerging technologies based on iconic classifiers recently proved to be good candidates for the implementation of accurate and real-time solutions, characterized by simplicity and automatic fast training stages.In this work the combined use of binary classifiers and iconic data reduction, based on Sanger neural networks, is proposed, detailing critical aspects related to the application of this approach to the specific problem of driving assistance. In particular it is investigated the possibility of a simplified learning stage, based on a small dictionary of poses, that makes the system almost independent from the actual user.On-board experiments demonstrate the effectiveness of the approach, even in case of noise and adverse light conditions. Moreover the system proved unexpected robustness to various categories of users, including people with beard and eyeglasses. Temporal integration of classification results, together with a partial distinction among visual distraction and fatigue effects, make the proposed technology an excellent candidate for the exploration of adaptive and user-centered applications in the automotive field.