Driver information system: a combination of augmented reality,deep learning and vehicular Ad-hoc networks

Improving traffic safety is one of the important goals of Intelligent Transportation Systems (ITS). In vehicle-based safety systems, it is more desirable to prevent an accident than to reduce severity of injuries. Critical traffic problems such as accidents and traffic congestion require the development of new transportation systems. Research in perceptual and human factors assessment is needed for relevant and correct display of this information for maximal road traffic safety as well as optimal driver comfort. One of the solutions to prevent accidents is to provide information on the surrounding environment of the driver. Augmented Reality Head-Up Display (AR-HUD) can facilitate a new form of dialogue between the vehicle and the driver; and enhance ITS by superimposing surrounding traffic information on the users view and keep drivers view on roads. In this paper, we propose a fast deep-learning-based object detection approaches for identifying and recognizing road obstacles types, as well as interpreting and predicting complex traffic situations. A single convolutional neural network predicts region of interest and class probabilities directly from full images in one evaluation. We also investigated potential costs and benefits of using dynamic conformal AR cues in improving driving safety. A new AR-HUD approach to create real-time interactive traffic animations was introduced in terms of types of obstacle, rules for placement and visibility, and projection of these on an in-vehicle display. The novelty of our approach is that both global and local context information are integrated into a unified framework to distinguish the ambiguous detection outcomes, enhance ITS by superimposing surrounding traffic information on the users view and keep drivers view on roads.     

Visual attention and the transition from novice to advanced driver

Inexperienced drivers are particularly vulnerable to road traffic accidents, and inattention emerges as a factor in these accidents. What do these drivers attend to and how can their observation skills be developed? When drivers scan the road around them, differences are observed as function of driving experience and training, with experienced drivers increasing their visual scanning on roadways of increasing complexity. Trained police drivers showed this effect of increased scanning even more than experienced drivers. This suggests that the driver's understanding of the task develops with experience, such that roads that demand increased monitoring (e.g. interweaving traffic on a multi-lane highway) receive more extensive scanning than roads that are simpler (e.g. light traffic on a straight rural road). Novice drivers do not show this sensitivity to road complexity, suggesting that they fail to attend to potential dangers involving the behaviour of other road users. Encouragingly, a simple training intervention can increase the visual scanning of novices.     

基于VR的驾驶仿真及安全教育系统开发

针对全国道路交通事故高发现状及传统驾驶安全教育方式单一、培训效果差的缺点,基于虚拟现实技术（VR）,在引发交通事故人为因素理论基础上,开发驾驶仿真及安全教育系统。系统基于Unity3D引擎,构建了基于道路实景数据的虚拟场景,并联合SUMO实现了道路交通流仿真,通过VR技术仿真驾驶环境及驾驶行为;基于碰撞检测原理,建立了关卡违规触发机制,编码自定义屏幕空间渲染方式模拟驾驶员视觉效果,并构建了基于图像的交通事故现场三维全景,从认知、感知层面培训驾驶员安全驾驶。实用性测试结果表明,系统实现了不同道路场景、气象条件与交通状况下的驾驶模拟及安全培训,增强了使用者的学习兴趣,提高了使用者驾驶安全素养,具有较强的实用性。     

A collaborative multi-agent message transmission mechanism in intelligent transportation system – A smart freeway example

Although drivers obtain road information through radio broadcasting or specific in-car equipment, there is still a wide gap between the synchronization of information and the actual conditions on the road. In the absence of adequate information, drivers often react to conditions with inefficient behaviors that do not contribute to their own driving goals, but increase traffic complication. Therefore, this study applies the features of information exchanged between “Multi-Agents” and mutual communication and collaboration mechanisms to intelligent transportation systems (ITS). If drivers could achieve distributed communication, share their driving information, and submit their own reasoned driving advice to others, many traffic situations will improve effectively. Additionally, the efficiency of the computing processes could have improved through distributed communication. At the same time, this paper proposes an architecture design, including vehicle components, OBU (On-Board Unit) devices and roadside device components (Roadside Unit) with hybrid architecture, which is intended to establish intelligent diversified road services to provide information support and applications.     

车路协同路侧感知融合方法的研究

随着智能网联汽车技术和产业的不断发展,智能网联汽车逐渐成为人们交通出行的选择之一。但受智能网联汽车自身环境感知系统对特定道路交通场景信息处理的局限,无法实现在所有行驶工况下安全高效的运行,其需车路协同路侧感知技术的辅助方能更安全高效的运行。海量的车路协同感知数据是城市道路和高速公路车路协同、运行分析和科学管理的宝藏,理解和分析这些数据是车路协同路侧感知融合的关键。面对车路协同路侧多传感器的不同数据,如何高效准确地挖掘和提取雷达、视频在不同时间、不同空间维度的数据,实现对重点交通场景(如视野盲区、急转弯道、隧道、桥梁)和交通事件、环境、设施安全等的雷达、视频数据进行快速融合检测、识别与检索,通过蜂窝车联网C-V2X网络在一定时延范围内有效地将路侧感知融合结果数据发送给智能网联汽车,确保其安全高效的行驶,是面向智能网联汽车辅助驾驶的车路协同路侧感知融合的关键问题。基于智能网联汽车其自身环境感知能力,对道路智能基础设施感知网络中的多传感器融合方法进行研究分析,提出了基于误差方差的多传感器融合算法,与非智能道路相比,其效率更高,更加智能化,可有效解决道路交通运行环境中存在的常见问题,为人们提供更加安全、高效、优质的交通出行服务。     

Drivers’ Performance Examination using a Personalized Adaptive Curve Speed Warning: Driving Simulator Study

ABSTRACT

A great number of traffic accidents occur during curve negotiation maneuvers. Most of these accidents could be avoided if drivers are provided with information that better guide them through the maneuver. Advanced Driver Assistance Systems (ADAS) such as Curve Speed Warning (CSW) have shown to be effective to improve safety on curve maneuvers by warning drivers of the speed required to make a safe curve maneuver. The effectiveness of such warning systems can potentially improve if the warnings are not only adapted to road and traffic condition but also adapted to individual drivers’ behavior. In this study, an Adaptive Curve Speed Warning (ACSW) system is developed that presents drivers a two-level visual and audio warning considering the variation in individual drivers’ perception-reaction time (PRT). The warning timing is adjusted according to a reward/punishment function to reinforce safer actions while providing an individualized in-time warning. Next, within a driving simulator environment, drivers’ performance using ACSW is compared to a CSW that does not consider PRT variation among drivers. Further, variation in drivers’ performance on curve maneuvers is discussed with respect to drivers’ approaching speed, variation in PRT, and braking behavior. Results show that drivers’ interaction with warning systems varies significantly based on their age and gender. In addition, results show how drivers approaching speed to a curve varies significantly based on road characteristics such as curve advisory speed and curve direction. Results from this study contribute to the development of more intelligent ADAS that could improve drivers’ comfort and safety.     

Insurance Classifications and Drivers' Galvanic Skin Response

Drivers' galvanic skin responses were recorded in town traffic and on country roads. Two groups of drivers were compared, those who would pay higher insurance premiums, by reason of their ago or occupation, and the rest. The high insurance group did not differ from the rest in the magnitude of the GSR. but when the ratio of the GSR in town traffic to that on a country road was considered there was a significant difference such that the high insurance group had a relatively greater GSR on the country road.

It is suggested that in town traffic most of the GSR arc generated by the actions of other drivers, whereas on the open road the GSRs are self generated by the subjects' driving. The GSR on the open road will therefore be more affected by the individual method of driving and by the risks the driver takes. The ratio of town GSR to country GSR can be used to eliminate the large unexplained differences between subjects and to expose the differences in driving behaviour     

An empirical study of subjective road categorization

Abstract

Driver behaviour will partially be a function of the cognitive schemata by which drivers represent features, functions and operations of traffic system components. This paper uses the Repertory Grid Technique to address the questions: (1) How do drivers internally represent different categories of road? (2) How are those representations mapped on to ‘official’ road categories?

A factor analysis of data obtained from 40 subjects revealed, in answer to question (1), that four factors, namely, ‘safety’; ‘urban/rural’; ‘road boundaries/slip-roads’; and ‘markings’; accounted for 82·3% of the variance. Multidimensional scaling and clustering analyses showed that subjective categorizations of roads are closely related to their objective physical characteristics, but less-well determined by ‘official’ road categories.

Certain features (e.g. slip-roads, markings, obstacle-free boundaries) appear to contribute little to the subjective safety of roads. This suggests that the potential benefits of these safety features may be fully achieved since risk compensation mechanisms will not operate.     

Drivers' margins of safety considered as a focus for research on error

Accident statistics alone cannot provide a sound understanding of driver error, although they can assist the evaluation of remedial measures against errors and accidents. Roadside observation of drivers' errors can provide a valid index of their relative riskiness and of overall accident frequency, but only in route-specific applications. Field testing of hypotheses developed from theories of driver error is seen to be a far more valid and arguably more cost-effective method of improving road safety than relying on post hoc subjective assessments of error contributions to accident statistics. The distinction between driving task and envronmental factors which contribute to error production and those which constrain error correction is not well-documented in road accident studies. Yet it seems essential to make this distinction if we are to reach a sound understanding of research requirements in this field and hence identify and evaluate cost effective countermeasures against driver error. The bias which certain drivers appear to have towards inadequate safety margins is seen to provide an instructive theoretical framework for field studies of error production and error correction as contributory factors in traffic accident causation.     

时间递推耦合神经网络的交通路径动态诱导技术

通过"人—机—环境"耦合关系,对路况与时间变化关系进行研究。综合车辆运行过程中不同时段的路况差异和人因作用、突发道路事故随机性,以神经网络有师学习作为经验累积方法,提出时间递推预测方法确定路径最短时间,从而实现对交通路径的动态诱导。递推预测以知识库累积经验与实时路况信息作比较,为驾驶者提供实时有效的路况信息支撑。结果表明,该诱导技术可辅助驾驶者及时对路况作出正确判断,减少因经验不足和突发事件造成的时间损失,适用于安装有GPS导航的车辆。实例分析表明,所构建模型与实际数据结合收到良好效果。     

Round-trip driving effects on driving performances and mental workload under different traffic rules

This study investigates the effect of switching between different traffic rules (left-versus right-hand traffic) on driving performance and mental workload. A driving simulation environment was developed according to the real environment. Two urban roads with different traffic systems were simulated. Twenty participants executed intersection turns and continuous car-following behavior in four simulated driving stages, including driving with familiar, unfamiliar, second time unfamiliar, and back to familiar traffic rules. The mean and standard deviations for speed, distance headway, and the standard deviation of lateral position were recorded as driving performance. Mental workload was determined using the NASA-TLX and Rating Scale Mental Effort questionnaires. One-way analysis of variance was used to evaluate the differences between the four driving stages using subjective and objective measures. The results showed that significant differences were obtained in all measures when driving in the four driving situations, except for the speed standard deviation. The car-following behavior was the most unsafe (significantly larger standard deviations for distance headway and mental workload) when driving in unfamiliar road traffic compared with the other stages. When driving under unfamiliar traffic rules for the second time, the mental workload was significantly relieved and the lane-keeping ability significantly improved. The results indicated that providing an adaptive runway for drivers to familiarize themselves with different traffic rules is necessary to improve driving performance and safety. These findings provide useful information for designing bridges linking two places with different traffic rules to increase traffic safety.     

Road-edge delineation in rural areas: effects on driving behaviour

When driving on lower-category Dutch rural roads without any delineation, drivers are likely to drift off the road with their right-side wheels, thus incurring damage to the pavement edge or even leading to accidents. In two experiments, two types of road-edge delineation, with continuous or dashed edge lines, were compared with two control roads without lines or with only a dashed line on the road axis. The first experiment consisted of non-obtrusive video recordings of passing traffic. Vehicle position on the experimental roads was more to the road's centre than on the control roads. The second experiment was a driving test with an instrumented vehicle, during daytime lighting and during darkness. Again, vehicle lateral position was more central on the experimental roads, especially during darkness. Subjects could safely pass oncoming vehicles. Driving speed increased on the experimental roads compared with the unlined control road, but not beyond speeds found on the axis-lined control road. Driver's mental effort while driving over the experimental roads did not differ from the effort while driving over the control roads. Subjectively rated effort was higher for the unlined control road than for the three other roads. Subjects preferred the edge-lined roads to the unlined control road, but not more than the axis-lined control road. It was concluded that edge-lines may provide a simple and effective way of inducing a more favourable lateral position on rural roads without having negative effects on subjective appraisal, driving performance or mental workload.     

Road-edge delineation in rural areas: effects on driving behaviour

When driving on lower-category Dutch rural roads without any delineation, drivers are likely to drift off the road with their right-side wheels, thus incurring damage to the pavement edge or even leading to accidents. In two experiments, two types of road-edge delineation, with continuous or dashed edge lines, were compared with two control roads without lines or with only a dashed line on the road axis. The first experiment consisted of non-obtrusive video recordings of passing traffic. Vehicle position on the experimental roads was more to the road's centre than on the control roads. The second experiment was a driving test with an instrumented vehicle, during daytime lighting and during darkness. Again, vehicle lateral position was more central on the experimental roads, especially during darkness. Subjects could safely pass oncoming vehicles. Driving speed increased on the experimental roads compared with the unlined control road, but not beyond speeds found on the axis-lined control road. Driver's mental effort while driving over the experimental roads did not differ from the effort while driving over the control roads. Subjectively rated effort was higher for the unlined control road than for the three other roads. Subjects preferred the edge-lined roads to the unlined control road, but not more than the axis-lined control road. It was concluded that edge-lines may provide a simple and effective way of inducing a more favourable lateral position on rural roads without having negative effects on subjective appraisal, driving performance or mental workload.     

Effects of road surface appearance and low friction warning systems on driver behaviour and confidence in the warning system

Warning systems for slippery road conditions are a potential newcomer among driver support systems. A total of 75 participants drove in a high-fidelity driving simulator on roads with both visible and invisible ice, to investigate to which extent drivers rely on a low friction warning system. Three experimental groups with different versions of a low friction warning system and a control group without warning system were compared. All drivers ranked the systems according to trust. A system displaying recommended speed received the best ratings. Driving speed was analysed for three particular segments of the route. Generally, lowest speeds were achieved with the recommended speed system. The participants drove more slowly on a slippery segment that looked icy than on the segments that looked dry when they did not receive a low friction warning. When they received a warning for low friction they also lowered their speed for the segment looking like asphalt. The results provide guidelines for how to present low friction warnings to drivers. The design has substantial effects on the resulting behaviour and therefore it can have a high impact on traffic safety. So far, not much research on low friction warning systems has been reported.     

Effects of road surface appearance and low friction warning systems on driver behaviour and confidence in the warning system

Warning systems for slippery road conditions are a potential newcomer among driver support systems. A total of 75 participants drove in a high-fidelity driving simulator on roads with both visible and invisible ice, to investigate to which extent drivers rely on a low friction warning system. Three experimental groups with different versions of a low friction warning system and a control group without warning system were compared. All drivers ranked the systems according to trust. A system displaying recommended speed received the best ratings. Driving speed was analysed for three particular segments of the route. Generally, lowest speeds were achieved with the recommended speed system. The participants drove more slowly on a slippery segment that looked icy than on the segments that looked dry when they did not receive a low friction warning. When they received a warning for low friction they also lowered their speed for the segment looking like asphalt. The results provide guidelines for how to present low friction warnings to drivers. The design has substantial effects on the resulting behaviour and therefore it can have a high impact on traffic safety. So far, not much research on low friction warning systems has been reported.     

Control of connected and automated vehicles: State of the art and future challenges

Autonomous driving technology pledges safety, convenience, and energy efficiency. Its challenges include the unknown intentions of other road users: communication between vehicles and with the road infrastructure is a possible approach to enhance awareness and enable cooperation. Connected and automated vehicles (CAVs) have the potential to disrupt mobility, extending what is possible with driving automation and connectivity alone. Applications include real-time control and planning with increased awareness, routing with micro-scale traffic information, coordinated platooning using traffic signals information, and eco-mobility on demand with guaranteed parking. This paper introduces a control and planning architecture for CAVs, and surveys the state of the art on each functional block therein; the main focus is on techniques to improve energy efficiency. We provide an overview of existing algorithms and their mutual interactions, we present promising optimization-based approaches to CAVs control and identify future challenges.     

Development of a measure model for optimal planning of maintenance and improvement of roads

In recent years, the spectacular increase of traffic in the majority of the developed countries has led to heightened concerns about the state of the roads, particularly in regard to how to improve driving conditions and, consequently, lower accident rates.There are many facets that influence the quality of roads, such as design characteristics (width, ditches, etc.) or their maintenance (signs, marking, etc.). All of them must be considered with a series of indicators when analyzing different roads.This work develops a methodology, based on the formulation of the Rasch model, that allows us to obtain an objective measure of road conditions, and later to evaluate each of the indicators that describe the road’s condition. As an example, data relating to the network of local and regional roads in the province of Badajoz, Spain, have been processed.Finally, results were implemented in a geographical information system (GIS) to visualize the distribution of all roads depending on their condition and, moreover, to make individual diagnoses of each road stretch possible, with the aim of optimizing resources allocation.     

Application of cellular automata and type-2 fuzzy logic to dynamic vehicle path planning

Nowadays, most road navigation systems’ planning of optimal routes is conducted by the On Board Unit (OBU). If drivers want to obtain information about the real-time road conditions, a Traffic Message Channel (TMC) module is also needed. However, this module can only provide the current road conditions, as opposed to actually planning appropriate routes for users. In this work, the concept of cellular automata is used to collect real-time road conditions and derive the appropriate paths for users. Notably, type-2 fuzzy logic is adopted for path analysis for each cell established in the cellular automata algorithm. Besides establishing the optimal routes, our model is expected to be able to automatically meet the personal demands of all drivers, achieve load balancing between all road sections to avoid the problem of traffic jams, and allow drivers to enjoy better driving experiences. A series of simulations were conducted to compare the proposed approach with the well-known A* Search algorithm and the latest state-of-the-art path planning algorithm found in the literature. The experimental results demonstrate that the proposed approach is scalable in terms of the turnaround times for individual users. The practicality and feasibility of applying the proposed approach in the real-time environment is thus justified.     

Insight into cooperation processes for traffic scenarios: modelling with naturalistic decision making

With the introduction of automatic vehicle guidance (AV), mixed traffic scenarios between automatically and manually guided vehicles are to be expected, at least during a transitions phase. To ensure the safety of motor vehicle transportation, it will be essential to develop a cooperative relationship between human drivers and AV. Research in this area is currently being done to gain insight into the manner of human drivers’ decisions to transfer the same behaviours to automatic vehicle guidance. A lot of research is being done to prepare for the introduction of AV, but there is still a lack of information on how individual road users make decisions in cooperative decisions. Currently, there is no study that has tried to understand the decision-making process with the help of an online-survey. For that reason, a questionnaire study on cooperative traffic situations (N?=?281) was carried out, which was analysed with the Natural Decision Making approach. By means of the NDM approach and the use of the recognition module, links between planned action and the expected action between road users were identified. Furthermore, it was possible to categorize individual communication signals into offensive or defensive signals and thus make predictions about the intention of the driver. These findings can be used to derive design recommendations for automatic vehicle guidance in cooperative situations.     

A visual reasoning-based approach for driving experience improvement in the AR-assisted head-up displays

Enabled by advanced data analytics and intelligent computing, augmented reality head-up displays (AR-HUDs) are appraised with a certain degree of intelligence towards an in-car assistance system providing more convenience for drivers and ensuring safer traffic. Nevertheless, current AR-HUDs systems fail to analyze perceptual results with recommended driving strategies as the cognitive intelligence, while solely rely on driver’s own decision-makings. To pave the way, this work stepwise proposes a visual reasoning-based approach for presenting drivers with perceptual, predictive, and reasoning information onto AR-HUDs toward cognitive intelligence. Firstly, a Driving Scenario Knowledge Graph comprising many road elements and empirical knowledge is established appropriately. Then, by analyzing the video streams and images collected by an in-car visual camera, the driving scene can be perceived comprehensively, including 1) identifying road elements and 2) moving elements’ intention recognition. Afterwards, a graph-based driving scenario reasoning model, driving scenario-adaptive KAGNET, is built for achieving driving strategy recommendations. Moreover, the analyzed information is shown on the HUDs via pre-defined AR graphics to support drivers intuitively. A case study is given lastly to prove its feasibility. As an explorative study, some limitations and future work are emphasized to attract further study and open discussion in this area for pursuing the better implementation of AR-HUDs.