驾驶分心检测方法综述

道路交通事故已经成为世界人民非自然死亡的主要原因,有学者认为90％的交通事故与驾驶员自身因素关联,而其中又有超过50％与驾驶分心相关.为了对驾驶员分心进行识别以及为下一步的分心预警干预提供结果判断,文章介绍了常见的驾驶分心检测与识别方法,包括人脸识别方法,眼睛瞳孔位置识别以及基于驾驶员生理信号(EEG)的分心监测方法....     

天津市道路汽车行驶工况的研究

本文研究了天津市的道路行驶工况，将一套车载实测系统安装在一辆华利牌微型面包车上研究天津,市不同道路的行驶状况。用速度、加速度等行驶特征参数为准则数，对天津市各种道路行驶状况作了详细的描述和分析，得出了一个基于实际汽车速度、加速度等准则数的行驶工况，并做了这个行驶工况和欧洲的ECE15、美国的FTP75和日本的10－15工况的对比。结果表明，天津市有它自己的道路行驶特征：市区平均速度低，怠速时间长，加减速频繁，平均加减速度小。     

A gradient boosting logit model to investigate driver’s stop-or-run behavior at signalized intersections using high-resolution traffic data

Driver’s stop-or-run behavior at signalized intersection has become a major concern for the intersection safety. While many studies were undertaken to model and predict drivers’ stop-or-run (SoR) behaviors including Yellow-Light-Running (YLR) and Red-Light-Running (RLR) using traditional statistical regression models, a critical problem for these models is that the relative influences of predictor variables on driver’s SoR behavior could not be evaluated. To address this challenge, this research proposes a new approach which applies a recently developed data mining approach called gradient boosting logit model to handle different types of predictor variables, fit complex nonlinear relationships among variables, and automatically disentangle interaction effects between influential factors using high-resolution traffic and signal event data collected from loop detectors. Particularly, this research will first identify a series of related influential factors including signal timing information, surrounding traffic information, and surrounding drivers’ behaviors using thousands drivers’ decision events including YLR, RLR, and first-to-stop (FSTP) extracted from high-resolution loop detector data from three intersections. Then the research applies the proposed data mining approach to search for the optimal prediction model for each intersection. Furthermore, a comparison was conducted to compare the proposed new method with the traditional statistical regression model. The results show that the gradient boosting logit model has superior performance in terms of prediction accuracy. In contrast to other machine learning methods which usually apply ‘black-box’ procedures, the gradient boosting logit model can identify and rank the relative importance of influential factors on driver’s stop-or-run behavior prediction. This study brings great potential for future practical applications since loops have been widely implemented in many intersections and can collect data in real time. This research is expected to contribute to the improvement of intersection safety significantly.     

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.     

Validation of the Rakha-Pasumarthy-Adjerid car-following model for vehicle fuel consumption and emission estimation applications

The Rakha-Pasumarthy-Adjerid (RPA) car-following model has been demonstrated to successfully replicate empirical driver car-following behavior. However, the validity of this model for fuel consumption and emission (FC/EM) estimation has yet to be studied. This paper attempts to address this research need by analyzing the applicability of the model for FC/EM estimation and comparing its performance to other state-of-practice car-following models; namely, the Gipps, Fritzsche and Wiedemann models. Naturalistic empirical data are employed to generate ground truth car-following events. The model-generated second-by-second Vehicle Specific Power (VSP) distributions for each car-following event are then compared to the empirical distributions. The study demonstrates that the generation of realistic VSP distributions is critical in producing accurate FC/EM estimates and that the RPA model outperforms the other three models in producing realistic vehicle trajectory VSP distributions and robust FC/EM estimates. This study also reveals that the acceleration behavior within a car-following model is one of the major contributors to producing realistic VSP distributions. The study further demonstrates that the use of trip-aggregated results may produce erroneous conclusions given that second-by-second errors may cancel each other out, and that lower VSP distribution errors occasionally result in greater bias in FC/EM estimates given the large deviation of the distribution at high VSP levels. Finally, the results of the study demonstrate the validity of the INTEGRATION micro-simulator, given that it employs the RPA car-following model, in generating realistic VSP distributions, and thus in estimating fuel consumption and emission levels.     

基于驾驶模拟器的通讯软件分心驾驶研究

驾驶车辆时使用手机通话或发短信会损害驾驶表现,但是,很少研究直接比较使用语音消息与文字消息的干扰作用。明确不同次任务对驾驶的影响是立法、车载设备设计和驾驶安全培训的基础。本文以驾驶模拟器与眼动仪为试验平台,比较了语音消息和文字消息对驾驶表现的影响。结果表明,语音消息和文字消息均会导致速度平均值、加速度和跟车距离发生更多变化,降低行车安全性。相较于文字消息,语音消息条件下车辆可以保持较好的横向稳定性与车道保持能力。在视觉行为方面,文字消息和语音消息均会减少驾驶员前方道路注视次数及注视时间,降低驾驶员道路交通信息的获取能力。     

Development and testing of a fully Adaptive Cruise Control system

Adaptive Cruise Control systems have been developed and introduced into the consumer market for over a decade. Among these systems, fully-adaptive ones are required to adapt their behaviour not only to traffic conditions but also to drivers’ preferences and attitudes, as well as to the way such preferences change for the same driver in different driving sessions. This would ideally lead towards a system where an on-board electronic control unit can be asked by the driver to calibrate its own parameters while he/she manually drives for a few minutes (learning mode). After calibration, the control unit switches to the running mode where the learned driving style is applied. The learning mode can be activated by any driver of the car, for any driving session and each time he/she wishes to change the current driving behaviour of the cruise control system.The modelling framework which we propose to implement comprises four layers (sampler, profiler, tutor, performer). The sampler is responsible for human likeness and can be calibrated while in learning mode. Then, while in running mode, it works together with the other modelling layers to implement the logic. This paper presents the overall framework, with particular emphasis on the sampler and the profiler that are explained in full mathematical detail. Specification and calibration of the proposed framework are supported by the observed data, collected by means of an instrumented vehicle. The data are also used to assess the proposed framework, confirming human-like and fully-adaptive characteristics.     

Real-world gaseous and particle emissions of a Bi-fuel gasoline/CNG Euro 6 passenger car

The objective of the present study is the assessment of the environmental impact of a bivalent (bi-fuel) vehicle, running either on gasoline or compressed natural gas (CNG). To that aim, a Euro 6 passenger car was tested under various real-world driving conditions. In order to cover the full range of conventional powertrains currently in the market, the tests were also repeated on a Euro 6 diesel passenger car. Both cars were driven in two routes, the first complying with the regulation limits and the second going beyond them. Carbon monoxide (CO), nitrogen oxides (NO_x) and particle number (PN) emissions were recorded using a Portable Emissions Measurement System (PEMS). Apart from the aggregated emission levels, in g/km, the exact emission location along the route was also assessed. Natural gas proved beneficial for CO and PN emissions, the level of which always remained below the respective legislation limits. On the other hand, under the dynamic driving conditions with gasoline, the relevant limits were exceeded. Cold start, occurring at the beginning of the urban part, and motorway driving were identified as major contributors to total emissions, especially in gasoline mode. However, the application of natural gas was associated with a penalty in NO_x emissions, which were significantly increased as compared to gasoline. Local peaks within the urban part were identified in CNG mode. In any case, the diesel vehicle was by far the highest NO_x emitter.     

Mental flexibility impairment in drivers with early Alzheimer's disease: A simulator-based study

After memory impairment, one of the most common troubles of early Alzheimer's disease (AD) is the impairment of executive functioning. However, it can have major consequences on daily life, notably on the driving activity. The present study focused on one important executive function involved in driving: mental flexibility; and considered how this impairment can affect driving. Ten patients with early AD were matched with 29 healthy older drivers. All participants were given an evaluation of mental flexibility through neuropsychological tests and an experimental test developed on a static driving simulator. The experiment was divided in two conditions; one without mental flexibility and another condition with a mental flexibility demand. AD patients showed impairments in the neuropsychological tests evaluating mental flexibility. These deficits are linked to the deficits they showed in the driving simulator flexibility tests. This study contributes to the understanding of mental flexibility mechanisms and on their role in driving activity. It also confirms that the driving simulator is a suitable tool to explore cognitive disorders and driving ability.