VIPER: an adaptive guidance and notification service system in internet of vehicles

In recent years, Internet of Vehicles has attracted increasing research attention, especially from the viewpoint of establishing effective information transmission methods to aid drivers and road users. Drivers can currently receive numerous types of assisted information. However, too much and cluttered information may affect their driving performance. Thus, effective guidance and notification services should be provided to drivers according to time, location, and events. For this purpose, we propose a Message Queue Telemetry Transport-based adaptive guide and notification service system called VIPER to provide driving assistance information. VIPER adaptively provides information to drivers and road users based on five conditions: Vehicle, points of Interest, People, Environment, and Roads. First, we establish a hierarchical grid architecture that is used to provide location-based services. Second, we collect information from the vehicles, roads, and environmental sensors to produce a weighted road network. Then, guide and notification services are provided based on this network. Thus, we can provide real-time driving assistance and help drivers to increase their safety and avoid traffic jams. We also analyze historical traffic data collected from vehicle detectors and accident data to estimate the safety and accident risk degrees of roads. To verify the feasibility of the proposed system, a system prototype is implemented to provide guidance and notification services. The experimental results show that our system can effectively assist drivers and road users and that it has a low system response time.

     

Obituary. Ted Megaw

Within the theoretical framework of control motivation, the effect of transient motivational variations (extrinsic to driving) on decision making in a simulated driving task was investigated. Young male drivers (mean age= 20.5 years), who were either novices or more experienced, participated in two experiments. In the first study (n = 45), the participants firstly carried out a reasoning task, extrinsic to driving, in which they randomly either failed (high control motivation), succeeded (low control motivation) or made aesthetic judgments with no evaluation in terms of success or failure (control group). Later, the participants had to decide whether to modify the given speed of a same vehicle for 38 driving situations presented in slide form. These situations were sorted into four categories according to the presence or absence of other road users and the presence or absence of an intersection. Compared with the control group, the participants of the failure group decide to make more speed changes as a function of the categories of driving situations and choose to make greater decelerations. Success leads the novices to discriminate less between the different categories of driving situations when making speed changes. A second study (n = 60) assessed whether high control motivation systematically induces a safer decision. The same driving task as in the first study was introduced by an instruction which made salient a representation of driving as being either cooperative or competitive. Whatever the instruction, the same results were found with the more experienced drivers: previous failure induces greater deceleration than success does. The opposite is observed for novices when driving was presented as a competitive activity, especially for intersection situations with no visible users. This discussion presents the usefulness of control motivation for understanding the drivers' regulation of their motivational states (allocation of attentional resources) and their representation of risk.     

Effect of driving experience on drivers' decoding process of roadway interpersonal communication

Sixty-three participants (32 novice, 31 experienced drivers) evaluated meanings of road users' signals in 24 traffic situations such as blinkers, headlights, hazard lamps and hand gestures. The traffic scenes were projected with a slide projector in a laboratory. Confidence in answers was also evaluated by using a five-point scale. The signals were classified into three categories: Formal Device-based Signal (Formal Signal), Informal Device-based Signal (Informal Signal), and Informal Gesture-based Signal (Everyday Signal). The total comprehension scores demonstrated that experienced drivers could understand the signals better than novice drivers. There was a large difference in the comprehension scores for Informal Signal between experienced and novice drivers. Novice drivers could understand Formal Signal and Everyday Signal better than Informal Signal. Similar results were also obtained in the confidence scores. Experienced drivers were more confident in their answers than novice drivers. An effect of gender was found in the scores of confidence. The discussion focuses on how driver's skill in interpersonal communication on roads develops in real traffic situations.     

Effect of driving experience on drivers' decoding process of roadway interpersonal communication

Sixty-three participants (32 novice, 31 experienced drivers) evaluated meanings of road users' signals in 24 traffic situations such as blinkers, headlights, hazard lamps and hand gestures. The traffic scenes were projected with a slide projector in a laboratory. Confidence in answers was also evaluated by using a five-point scale. The signals were classified into three categories: Formal Device-based Signal (Formal Signal), Informal Device-based Signal (Informal Signal), and Informal Gesture-based Signal (Everyday Signal). The total comprehension scores demonstrated that experienced drivers could understand the signals better than novice drivers. There was a large difference in the comprehension scores for Informal Signal between experienced and novice drivers. Novice drivers could understand Formal Signal and Everyday Signal better than Informal Signal. Similar results were also obtained in the confidence scores. Experienced drivers were more confident in their answers than novice drivers. An effect of gender was found in the scores of confidence. The discussion focuses on how driver's skill in interpersonal communication on roads develops in real traffic situations.     

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.     

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.     

Variability in decision-making and critical cue use by different road users at rail level crossings

Collisions at rail level crossings (RLXs) are typically high-severity and high-cost, often involving serious injuries, fatalities and major disruptions to the transport network. Most research examining behaviour at RLXs has focused exclusively on drivers and consequently there is little knowledge on how other road users make decisions at RLXs. We collected drivers’, motorcyclists’, bicyclists’ and pedestrians’ self-reported daily experiences at RLXs for two weeks, focusing on behaviour, decision-making and information use in the presence of a train and/or activated RLX signals. Both information use and behaviour differed between road users. Visual information (e.g. flashing lights) was more influential for motorists, whereas pedestrians and cyclists relied more on auditory information (e.g. bells). Pedestrians were also more likely to violate active RLX warnings and/or cross before an approaching train. These results emphasise the importance of adopting holistic RLX design approaches that support cognition and behaviour across for all road users.

Practitioner Summary: This study explores how information use and decision-making at rail level crossings (RLXs) differ between road user groups, using a two-week self-report study. Most users make safe decisions, but pedestrians are most likely to violate RLX warnings. Information use (visual vs. auditory) also differs substantially between road user groups.     

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.     

交通出行诱导的离散Stackelberg动态博弈模型及其求解算法

针对交通出行诱导的实际需要,分析了道路交通管理者和道路使用者的博弈策略和博弈特征.通过道路使用者信息的模糊模型.建立了交通出行诱导的离散动态Stackelberg博弈模型.分析了交通出行诱导的两阶段博弈特征,提出了基于逆向归纳法的博弈模型求解算法.通过对模型无约束转化,运用Monte-Carlo法对示例路网进行了计算机求解,求解结果表明产生的交通出行诱导方案能够实现系统最优下的用户最优.     

Human factors and road safety. Overview of research at the University of Michigan Transportation Research Institute between 1977 and 1986

This article presents a brief overview of the research performed at tge Human Factors Division of The University of Michigan Transportation Research Institute between 1977 and 1986. The focus of the research has been on human factors (ergonomics) aspects of road safety. Specifically, the research has dealt with the following issues: vehicle headlighting, vehicle rear lighting and signalling, vehicle displays and controls, vehicle components, conspicuity of vehicles, legibility of traffic signs and licence plates, driver reaction time, driver performance, stopping sight distance, driver seated position, individual differences (drivers with disabilities, older drivers), methods for measuring blood alcohol concentration, societal violence and traffic accidents, cross-cultural comparison of driver risk-perception, and theoretical issues.     

都市行车引导系统的模型研究（下）

4.2模型分析 将网络中的流分组,每组的大小由一个时间段内进入网络的流所确定。每组用第一辆车和最后一辆车的运行来仿真,用事故出现的频率、受影响的路段以及事故出现时间等参数仿真事故,当仿真事故时,路段容量发生变化。     

Efficient algorithm for automatic road sign recognition and its hardware implementation

The automatic detection of road signs is an application that alerts the vehicle’s driver of the presence of signals and invites him to react on time in the aim to avoid potential traffic accidents. This application can thus improve the road safety of persons and vehicles traveling in the road. Several techniques and algorithms allowing automatic detection of road signs are developed and implemented in software and do not allow embedded application. We propose in this work an efficient algorithm and its hardware implementation in an embedded system running in real time. In this paper we propose to implement the application of automatic recognition of road signs in real time by optimizing the techniques used in different phases of the recognition process. The system is implemented in a Virtex4 FPGA family which is connected to a camera mounted in the moving vehicle. The system can be integrated into the dashboard of the vehicle. The performance of the system shows a good compromise between speed and efficiency.     

The effect of communicational signals on drivers' subjective appraisal and visual attention during interactive driving scenarios

Communicational signals (e.g. lights and horns) are imperative for on-road interaction between drivers. The aim of the present study was to explore how these signals affect drivers' subjective appraisal and visual attention, and how drivers decode the signals from other vehicles within a variety of interactive contexts. Twenty-five male participants (20 valid samples, ranging from 21 to 29 years of age) were recruited to watch film clips of pre-designed interactive scenarios involving common vehicle signals in a full-view simulator (i.e. including road view and mirror views). Participants' attitudes towards the interacting vehicle's behaviours, emotional responses, fixation metrics, and decoded meanings were recorded and analysed. The majority of tested signals, with the exception of the horn used in the behind vehicles, significantly improved drivers' attitudes and pleasure. All signals significantly increased emotional arousal, as well as the total fixation time and mean fixation duration on the interacting vehicle. When the interacting vehicle was visible in mirrors, the signal usage significantly increased the fixation frequency towards it. Meanwhile, a significant decrease in total fixation time and mean fixation duration on the road was reported. The results also demonstrated that the decoded signal contained several meanings simultaneously depending on both the signal type and its interactive context. This study quantified the communication process via vehicular signals under typical situations involving other vehicles, and also suggested new ideas on how to establish more advanced communication between drivers.     

一种具有语音交互功能的停车诱导系统设计

基于百度地图、讯飞语音,设计了一种具有语言交互功能、自动优化推荐停车场的停车诱导系统。该系统根据实时路况、目的地周围停车场空闲车位等信息,推荐停车场、优化行驶路线、采用语音交互技术诱导用户进场停车。车辆行驶过程中不断刷新数据,前方道路出现拥堵时,及时用语音提醒用户。针对用户的不同需求,调整优化权重,给出个性化推荐。该系统的应用既能提高停车场的资源利用率,又能提高经济和社会效益。     

城市路网交通分散协调控制

针对城市路网交通系统规模大和非线性、不确定性强等特点，利用模糊神经网络设计了一种新的实时分散协调控制算法．把城市区域和市内快速公路作为一个路网大系统，子系统为路网中的各个交叉口；每个子系统都有一个模糊神经网络控制器，该控制器根据它自己和相邻子系统的交通流信息来动态管理相序及绿灯时间．控制器由3个模块组成：相序选择模块、绿灯判断模块和相位切换模块．控制器的控制目标是保持快速公路主线密度均衡和区域内各车辆平均延误时间最短．仿真研究表明，该算法能有效处理各种路网交通环境．     

Multi-Level Planning for Semi-autonomous Vehicles in Traffic Scenarios Based on Separation Maximization

The planning of semi-autonomous vehicles in traffic scenarios is a relatively new problem that contributes towards the goal of making road travel by vehicles free of human drivers. An algorithm needs to ensure optimal real time planning of multiple vehicles (moving in either direction along a road), in the presence of a complex obstacle network. Unlike other approaches, here we assume that speed lanes are not present and that different lanes do not need to be maintained for inbound and outbound traffic. Our basic hypothesis is to carry forward the planning task to ensure that a sufficient distance is maintained by each vehicle from all other vehicles, obstacles and road boundaries. We present here a 4-layer planning algorithm that consists of road selection (for selecting the individual roads of traversal to reach the goal), pathway selection (a strategy to avoid and/or overtake obstacles, road diversions and other blockages), pathway distribution (to select the position of a vehicle at every instance of time in a pathway), and trajectory generation (for generating a curve, smooth enough, to allow for the maximum possible speed). Cooperation between vehicles is handled separately at the different levels, the aim being to maximize the separation between vehicles. Simulated results exhibit behaviours of smooth, efficient and safe driving of vehicles in multiple scenarios; along with typical vehicle behaviours including following and overtaking.     

Parallel Planning: A New Motion Planning Framework for Autonomous Driving

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as "parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality. A deep planning model which combines a convolutional neural network (CNN) with the Long Short-Term Memory module (LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human drivers. Moreover, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder (VAE) and a generative adversarial network (GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.      

Edge-centric trust management in vehicular networks

A major objective of vehicular networking is to improve road safety and reduce traffic congestion. The experience of individual vehicles on traffic conditions and travel situations can be shared with other vehicles for improving their route planning and driving decisions. Nevertheless, the frequent occurrence of adversary vehicles in the network may affect the overall network performance and safety. These vehicles may behave intelligently to avoid detection. To effectively control and monitor such security threats, an efficient Trust Management system should be employed to identify the trustworthiness of individual vehicles and detect malicious drivers which is the major focus of this work. We propose a hybrid solution, which integrates Edge Computing and Multi-agent modeling in a Trust Management system for vehicular networks. The proposed solution also aims to overcome the limitations of the two commonly utilized approaches in this context: cloud computing and Peer-to-Peer (P2P) networking. Our framework has a set of features that make it an efficient platform to address the major security challenges in vehicular networks including latency, scalability, uncertainty, data accessibility, and malicious behavior detection. Performance of the approach is evaluated by simulating a realistic environment. Experimental results show that the proposed approach outperforms similar approaches from literature for various performance indicators.     

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.     

Making driver modeling attractive

Automobile industry and academic researchers expend considerable effort to develop driver assistance systems. DASs are aware of certain driving situations and support drivers through information, warnings, or even intervention. Many DAS applications are safety oriented, such as lane departure warning systems. Some are comfort oriented, such as automated parking assistants. Moreover, DAS human-machine interfaces must support careful communication with potentially taxed drivers. Driver models support DAS design in several ways. Our work focuses on modeling the tactical level of driving decisions, such as when to brake and whether to accelerate and pass another vehicle. Such decisions are based on local, instantaneously available environmental information about the road and other cars in the same or an adjacent lane.