Driver behaviour at rail level crossings: responses to flashing lights, traffic signals and stop signs in simulated rural driving

Australian road and railway authorities have made a concerted effort to reduce the number of rail level crossings, particularly the higher risk passive crossings that are protected by devices such as ‘give way’ or ‘stop’ signs. To improve this situation, passive level crossings are often upgraded with active controls such as flashing red lights. Traffic signals may provide good safety outcomes at level crossings but remain untested. The primary purpose of this research was to compare driver behaviour at two railway level crossings with active controls, flashing red lights and traffic signals, to behaviour at the current standard passive level crossing control, a stop sign. Participants drove the MUARC advanced driving simulator for 30 min. During the simulated drive, participants were exposed to three level crossing scenarios. Each scenario consisted of one of three level crossing control types, and was associated with an oncoming train. Mean vehicle speed on approach to the level crossings decreased more rapidly in response to flashing lights than to traffic signals. While speed on approach was lowest for the stop-sign condition, the number of non-compliant drivers (i.e., those who did not stop) at the crossing was highest for this condition. While results indicate that traffic signals at rail level crossings do not appear to offer any safety benefits over and above flashing red lights, further avenues of research are proposed to reach more definitive conclusions. Compliance was lowest for the passive crossing control which provides further support for the ongoing passive crossing upgrades in Australia.     

动态场景下的交通标识检测与识别研究进展

动态环境下交通标识的自动识别随着无人自主驾驶汽车对环境理解的要求提高引起了人们的高度关注.近年来研究人员从检测、跟踪和识别等方面展开了对这一问题的深入研究.针对动态场景下基于单个普通光学摄像机的交通标识检测与识别方法做一系统回顾.重点围绕交通标识的检测、识别,时序信息的利用等展开.此外,还对这一领域目前常用的数据集和评价方法做了具体介绍.最后指出了未来发展的趋势和方向.     

A belief-based sequential fusion approach for fusing manual signs and non-manual signals

Most of the research on sign language recognition concentrates on recognizing only manual signs (hand gestures and shapes), discarding a very important component: the non-manual signals (facial expressions and head/shoulder motion). We address the recognition of signs with both manual and non-manual components using a sequential belief-based fusion technique. The manual components, which carry information of primary importance, are utilized in the first stage. The second stage, which makes use of non-manual components, is only employed if there is hesitation in the decision of the first stage. We employ belief formalism both to model the hesitation and to determine the sign clusters within which the discrimination takes place in the second stage. We have implemented this technique in a sign tutor application. Our results on the eNTERFACE’06 ASL database show an improvement over the baseline system which uses parallel or feature fusion of manual and non-manual features: we achieve an accuracy of 81.6%.     

Evaluating the effects of bilingual traffic signs on driver performance and safety

Variable message signs (VMS) can provide immediate and relevant information to road users and bilingual VMS can provide great flexibility in countries where a significant proportion of the population speak an alternative language to the majority. The study reported here evaluates the effect of various bilingual VMS configurations on driver behaviour and safety. The aim of the study was to determine whether or not the visual distraction associated with bilingual VMS signs of different configurations (length, complexity) impacted on driving performance. A driving simulator was used to allow full control over the scenarios, road environment and sign configuration and both longitudinal and lateral driver performance was assessed. Drivers were able to read one- and two-line monolingual signs and two-line bilingual signs without disruption to their driving behaviour. However, drivers significantly reduced their speed in order to read four-line monolingual and four-line bilingual signs, accompanied by an increase in headway to the vehicle in front. This implies that drivers are possibly reading the irrelevant text on the bilingual sign and various methods for reducing this effect are discussed.     

Linux下PCI设备驱动开发方法及应用实例

Linux是一种日趋成熟完善的操作系统.越来越多的软硬件厂商开始使用Linux平台来开发自己的产品,因而对基于该平台的设备驱动程序的需求也愈来愈多。同时PCI总线作为一个具有兼容性强、平台无关性等特点从的计算机总线标准。日前得到了广泛的应用。本文通过实例讨论了如何在Linux下实现PCI设备驱动程序的开发。     

语音信号端点检测方法综述及展望*

端点检测是语音信号处理过程中非常重要的一步,它的准确性直接影响语音信号处理的速度和结果,因此端点检测方法的研究,特别是在噪声环境下端点检测的研究,一直是语音信号处理中的热点。从基于时域参数、频域参数、时频参数、模型匹配等方法的角度,较全面地回顾了端点检测方法的发展历程,对各种方法的优缺点进行了比较分析,并给出了这些方法的改进意见,对端点检测未来的研究方向进行了展望。     

用分层循环遗传算法去识别生理信号情感状态

针对生理信号的情感识别问题,采用自适应分层式遗传算法方法对生理信号进行特征选择,能有效地解决特征组合优化问题。实验仿真表明,可以得到有效地特征组合来进行生理信号情感状态识别。     

基于ARM和FPGA的高扩展性超声检测模块设计与实现

介绍了一种以ARM和FPGA联合作为中央控制处理单元的4路超声探伤模块。给出了其整体结构方案,阐述了以4路超声模拟信号为一组的多路超声探伤模块硬件扩展的设计思路和实现方案,讨论了FPGA对高速LVDS数据的采集、处理、时序同步功能的实现,ARM与FPGA之间总线接口的实现,ARM嵌入式系统功能以及网络通信功能的实现。实际应用表明,该功能模块能达到预期的设计要求,并能方便地实现硬件扩展。     

Onset detection of ultrasonic signals for the testing of concrete foundation piles by coupled continuous wavelet transform and machine learning algorithms

The construction of ultra-high-rise and long-span structures requires higher requirements for the integrity detection of piles. The acoustic signal detection has been verified an efficient and accurate nondestructive testing method. In fact, the integrity of piles is closely related to the onset time of signals. The accuracy of onset time directly affects the integrity evaluation of a pile. To achieve high-precision onset detection, continuous wavelet transform (CWT) preprocessing and machine learning algorithms were integrated into the software of high-sampling rate testing equipment. The distortion of waveforms, which could interfere with the accuracy of detection, was eliminated by CWT preprocessing. To make full use of the collected waveform data, three types of machine learning algorithms were used for classifying whether the data points are ambient or ultrasonic signals. The models involve a commonly used classifier (ELM), an individual classification tree model (DTC), an ensemble tree model (RFC) and a deep learning model (DBN). The classification accuracy of the ambient and ultrasonic signals of these models was compared by 5-fold validation. Results indicate that RFC performance is better than DBN and DTC after training. It is more suitable for the classification of points in waveforms. Then, a detection method of onset time based on classification results was therefore proposed to minimize the interference of classification errors on detection. In addition to the three data mining methods, the autocorrelation function method was selected as the control method to compare the proposed data mining based methods with the traditional one. The accuracy and error analysis of 300 waveforms proved the feasibility and stability of the proposed method. The RFC-based detection method is recommended because of the highest accuracy, lowest errors, and the most favorable error distribution among four onset detection methods. Successful applications demonstrate that it could provide a new way for ensuring the accurate testing of pile foundation integrity.     

The effects of variable mechanical parameters on peak power and energy consumption of ankle-foot prostheses at different speeds

In recent years, powered ankle-foot prostheses are developed to restore human ankle behaviour. Compared with common passive ankle prostheses, a powered ankle-foot prosthesis can provide amputees with better performance on walking gait by providing additional energy. However, the electrical peak power of powered ankle-foot prostheses at high speed is still a big challenge for the current actuation system. While most researchers focus on the energy consumption on one walking speed, we investigate the influences of several variable mechanical parameters to reduce electrical peak power during different locomotion speeds. Then we apply this concept to current ankle-foot models. It's found that the variable mechanical parameters can significantly reduce the demand of electrical peak power at different speeds. Meanwhile, the reduction of the electrical peak power does not lead to a large increment of the energy consumption. The results also show that every variable mechanical parameter has a different influence on the peak power and energy consumption of the ankle-foot prosthesis. It means the most effective and sensitive variable mechanical parameter need to be found for a specific ankle prosthesis system.     

The relationship between work productivity and acute responses at different levels of production standard times

Work productivity is typically associated with production standard times. Harder production standards generally result in higher work productivity. However, the tasks become more repetitive in harder production standard time and workers may be exposed to higher rates of acute responses which will lead to higher risks of contracting work-related musculoskeletal disorders (WMSDs). Hence, this paper seeks to investigate the relationship between work productivity and acute responses at different levels of production standard times. Twenty industrial workers performed repetitive tasks at three different levels of production standard time (PS), corresponding to “normal (PSN)”, “hard (PSH)” and “very hard (PSVH)”. The work productivity and muscle activity were recorded along these experimental tasks. The work productivity target was not attainable for hard and very hard production standard times. This can be attributed to the manifestations of acute responses (muscle activity, muscle fatigue, and perceived muscle fatigue), which increases as the production standard time becomes harder. There is a strong correlation between muscle activity, perceived muscle fatigue and work productivity at different levels of production standard time. The relationship among these variables is found to be significantly linear (R = 0.784, p < 0.01). The findings of this study are indeed beneficial to assess the existing work productivity of workers and serves as a reference for future work productivity planning in order to minimize the risk of contracting WMSDs.     

A fuzzy reasoning and fuzzy-analytical hierarchy process based approach to the process of railway risk information: A railway risk management system

Risk management is becoming increasingly important for railway companies in order to safeguard their passengers and employees while improving safety and reducing maintenance costs. However, in many circumstances, the application of probabilistic risk analysis tools may not give satisfactory results because the risk data are incomplete or there is a high level of uncertainty involved in the risk data. This article presents the development of a risk management system for railway risk analysis using fuzzy reasoning approach and fuzzy analytical hierarchy decision making process. In the system, fuzzy reasoning approach (FRA) is employed to estimate the risk level of each hazardous event in terms of failure frequency, consequence severity and consequence probability. This allows imprecision or approximate information in the risk analysis process. Fuzzy analytical hierarchy process (fuzzy-AHP) technique is then incorporated into the risk model to use its advantage in determining the relative importance of the risk contributions so that the risk assessment can be progressed from hazardous event level to hazard group level and finally to railway system level. This risk assessment system can evaluate both qualitative and quantitative risk data and information associated with a railway system effectively and efficiently, which will provide railway risk analysts, managers and engineers with a method and tool to improve their safety management of railway systems and set safety standards. A case study on risk assessment of shunting at Hammersmith depot is used to illustrate the application of the proposed risk assessment system.