Human driving data-based design of a vehicle adaptive cruise control algorithm

This paper presents a vehicle adaptive cruise control algorithm design with human factors considerations. Adaptive cruise control (ACC) systems should be acceptable to drivers. In order to be acceptable to drivers, the ACC systems need to be designed based on the analysis of human driver driving behaviour. Manual driving characteristics are investigated using real-world driving test data. The goal of the control algorithm is to achieve naturalistic behaviour of the controlled vehicle that would feel natural to the human driver in normal driving situations and to achieve safe vehicle behaviour in severe braking situations in which large decelerations are necessary. A non-dimensional warning index and inverse time-to-collision are used to evaluate driving situations. A confusion matrix method based on natural driving data sets was used to tune control parameters in the proposed ACC system. Using a simulation and a validated vehicle simulator, vehicle following characteristics of the controlled vehicle are compared with real-world manual driving radar sensor data. It is shown that the proposed control strategy can provide with natural following performance similar to human manual driving in both high speed driving and low speed stop-and-go situations and can prevent the vehicle-to-vehicle distance from dropping to an unsafe level in a variety of driving conditions.     

浅谈渐开线花键的检测方法的应用

本文对渐开线花键的检测方法进行介绍分析,为渐开线花键的生产检验及零件验收提供理论依据和参考。     

基于弹性波CT的大体积混凝土检测应用

现代土木工程时常涉及大体积混凝土,其施工及养护过程极易出现质量问题,因此项目实施过程中检测工作始终贯穿于各个环节。常规混凝土无损检测方法一般无法对混凝土内部大截面进行直观评判,而弹性波CT则一次性重构截面波速分布,经反演后直接推定剖面内缺陷存在与否以及缺陷的位置及规模。通过弹性波CT在大体积混凝土检测中的应用,并结合超声法等验证手段,验证了该方法分辨率高、可靠性好、图像直观等优点,为其他相关类型项目提供了有益的参考。     

声波 CT 技术在旧桥粘钢加固评价中的应用

在役桥梁运营维护中，粘钢加固技术由于其经济、便捷等特点是保证桥梁安全运营的重要手段。但由于现有检测手段的不足，旧桥粘钢加固后修复效果是未知的。为弥补检验修复效果这一空白，将CT技术引入旧桥粘钢加固评价当中，以安徽某大桥的工程为例，用声波CT技术可以检测加固区域混凝土整体质量，得到定性、定量的检测结果，为旧桥加固修复情况提供客观评价，且可增强后续处治的针对性，并节约维修成本。     

Adhesion estimation at the wheel–rail interface using advanced model-based filtering

The railway industry in the UK is currently expanding the use of condition monitoring of railway vehicles. These systems can be used to improve maintenance procedures or could potentially be used to monitor current vehicle running conditions without the use of cost prohibitive sensors. This paper looks at a novel method for the online detection of areas of low adhesion in the wheel/rail contact that cause significant disruption to the running of a network, particularly in the autumn season. The proposed method uses a Kalman–Bucy filter to estimate the creep forces in the wheel–rail contact area; post-processing is then applied to provide information indicative of the actual adhesion level. The algorithm uses data that, in practice, would be available from a set of modest cost inertial sensors mounted on the vehicle bogie and wheel-sets. The efficacy of the approach is demonstrated using simulation data from a nonlinear dynamic model of the vehicle and its track interface.     

Contents Index Volume 35 and 36

Active safety systems would benefit from tyre force and friction potential information. Different sensor concepts, including, among others, the EU–funded Apollo–project developed tyre sensor based on optical position detection, are being studied. The sensor can measure tyre carcass deflections with respect to the rim. The carcass deflections can be used to calculate tyre forces and they may be exploited in the estimation of friction potential. The waveforms of the sensor signal are illustrated. The vertical and lateral force estimations are presented with unavoidable compensation parts. The tyre sensor measurements were compared to the measurement–vehicle results and good correlations achieved. Continuing activities are concerned with the estimation of friction potential and the detection of aquaplaning.     

手动油泵溢油问题的工艺分析及解决

本文针对生产现场出现的液压手动油泵排气孔溢油现象,从液压翻转机构工作原理出发,分析手动油泵溢油产生的原因,使问题得以解决,取得良好效果。     

From Twitter to detector: Real-time traffic incident detection using social media data

The effectiveness of traditional incident detection is often limited by sparse sensor coverage, and reporting incidents to emergency response systems is labor-intensive. We propose to mine tweet texts to extract incident information on both highways and arterials as an efficient and cost-effective alternative to existing data sources. This paper presents a methodology to crawl, process and filter tweets that are accessible by the public for free. Tweets are acquired from Twitter using the REST API in real time. The process of adaptive data acquisition establishes a dictionary of important keywords and their combinations that can imply traffic incidents (TI). A tweet is then mapped into a high dimensional binary vector in a feature space formed by the dictionary, and classified into either TI related or not. All the TI tweets are then geocoded to determine their locations, and further classified into one of the five incident categories.We apply the methodology in two regions, the Pittsburgh and Philadelphia Metropolitan Areas. Overall, mining tweets holds great potentials to complement existing traffic incident data in a very cheap way. A small sample of tweets acquired from the Twitter API cover most of the incidents reported in the existing data set, and additional incidents can be identified through analyzing tweets text. Twitter also provides ample additional information with a reasonable coverage on arterials. A tweet that is related to TI and geocodable accounts for approximately 5% of all the acquired tweets. Of those geocodable TI tweets, 60–70% are posted by influential users (IU), namely public Twitter accounts mostly owned by public agencies and media, while the rest is contributed by individual users. There is more incident information provided by Twitter on weekends than on weekdays. Within the same day, both individuals and IUs tend to report incidents more frequently during the day time than at night, especially during traffic peak hours. Individual tweets are more likely to report incidents near the center of a city, and the volume of information significantly decays outwards from the center.     

道路桥梁检测数据结合BIM技术的应用研究

随着市政道路桥梁检测要求的不断提高,检测数据分析和应用深度也随之提高,如何让检测数据更加有效、更加生动的反应道路桥梁的技术状况,成为了越来越多使用单位的基本诉求.针对这一现象,论文介绍了道路桥梁检测数据与BIM技术结合的基本路径,分析阐述了道路桥梁检测数据的主要内容及应用方面的不足,重点研究了如何以BIM信息模型作为检测数据载体的实施方法,归纳了哪些检测数据更加迫切的需要导入到BIM模型中,并通过分析各方单位的不同需求,对使用BIM模型中检测数据的应用场景提出了实施方案,为BIM信息模型的建设和应用提供了新的思路.