Road safety investigation of the interaction between driver and cyclist

With growing global concern to reduce CO2 emissions, the transportation modal shift from car to bicycle is an encouraging alternative, which is getting more popular in Europe and North America, thanks to very low impact on the environment. On the other hand, the infrastructure for cyclist should be improved, since cyclists are vulnerable road users and with an increase in the number of cyclists the concern for their safety also gets increased. In this thesis, the analysis of accidents in which cyclists have been involved and understanding the reason for these accidents have been discussed, then the necessary requirements to design and implement a safe bicycle network is introduced. The study focuses on the drivers’ behavior in terms of interaction with cyclists when there is a presence of a cyclist crossing. Therefore the road safety investigation on cyclist infrastructure was made with observing drivers’interaction with cyclists. Then the time-based surrogacy measures used to investigate the safety level of the cylist, in particular PET (Post Encroachment Time) and TTC (Time to Collision) between driver and bicyclist were determing keeping in mind the right-angle collision. Furthermore we tried to find the reaction time of the drivers especially on signals and also with the presence of cyclist on the crossing to understand the time which is needed for the driver to stop the car. All of this data could be later useful for the reconstruction of the accidents. Understanding the instants at which driver applies the brakes was made possible by installing a V-Box device inside our test vehicle which also used to determine measures such as speed, distance and other important. Finally using mobile eye tracker the driver visual behavior when arriving the crossing point where observed and results showed that at number of situations driver’s gaze was distracted and only cyclist became an important focus only when he was at a considerable length from the crossing

Comparing eye-tracking system effectiveness in field and driving simulator studies

Background: Several studies have been developed by combining the benefits of eye-tracking systems with driving simulators to simultaneously investigate driving behavior and the potential source of distraction. However, little effort has been spent in terms of eye-tracking validation in the driving simulator environment. Objective: The overall aim of this study is to validate an eye-tracking system within the context of a driving simulation environment by considering a specific urban context application. Methods: Both a field survey and a driving simulation experiment have been developed for a case study located in Rome, Italy. The selected real road sections and events have been reproduced on the driving simulator system and an eye-tracking has been used to record the eye movements both on board of a real vehicle and on the simulator. The eye movements of 14 participants in the field survey and 18 participants in the driving simulation tests, as well as their driving performances, have been collected while approaching an urban intersection and in relation to two specific road events: i) the presence of a speed limit sign and ii) the presence of a crossing pedestrian. Results: Eye tracker parameters and driving performances were compared between the real driving tests and driving simulator experiments in order to validate the eye-tracking system. It has been validated for both the events in terms of duration and distance of the eye fixation. Conclusion: The results demonstrate that the eye-tracking system stands as an effective tool for studies and applications in a virtual reality environment

Driver Response to Simulated Intersections: An Analysis of Workload-Related Variables

A roadway intersection driving simulation was created to investigate driver information processing at intersections. Research participants were provided a visual simulation of approaching intersections using a video display with a 120 degree visual field. Six groups, each containing 12 subjects, were formed according to age and gender, with age ranging from 18 to 74 years. All participants viewed 14 separate intersections, which varied according to types of traffic control signs and signals. Individual workload was assessed in three categories of response: performance, subjective, and physiological. A MANOVA was performed on six dependent variables in the 3 (age) by 2 (gender) design. Results indicate significant main effects for both age and gender. The three significant dependent variables were pedal response errors, speed of response, and heart rate reactivity to each intersection. The responses suggest greater workloads for older drivers and female drivers. In addition to age and gender, a number of driver information processing characteristics were measured. Stepwise regressions indicated that performance decrements to the simulated driving situations could best be predicted by subjects\u27 scores for field dependency, visual acuity, and depth perception. However, age alone, accounted for more variance in performance than any single information processing variable

A multidisciplinary research approach for experimental applications in road-driver interaction analysis

This doctoral dissertation represents a cluster of the research activities conducted at the DICAM Department of the University of Bologna during a three years Ph.D. course. In relation to the broader research topic of “road safety”, the presented research focuses on the investigation of the interaction between the road and the drivers according to human factor principles and supported by the following strategies: 1) The multidisciplinary structure of the research team covering the following academic disciplines: Civil Engineering, Psychology, Neuroscience and Computer Science Engineering. 2) The development of several experimental real driving tests aimed to provide investigators with knowledge and insights on the relation between the driver and the surrounding road environment by focusing on the behaviour of drivers. 3) The use of innovative technologies for the experimental studies, capable to collect data of the vehicle and on the user: a GPS data recorder, for recording the kinematic parameters of the vehicle; an eye tracking device, for monitoring the drivers’ visual behaviour; a neural helmet, for the detection of drivers’ cerebral activity (electroencephalography, EEG). 4) The use of mathematical-computational methodologies (deep learning) for data analyses from experimental studies. The outcomes of this work consist of new knowledge on the casualties between drivers’ behaviour and road environment to be considered for infrastructure design. In particular, the ground-breaking results are represented by: - the reliability and effectiveness of the methodology based on human EEG signals to objectively measure driver’s mental workload with respect to different road factors; - the successful approach for extracting latent features from multidimensional driving behaviour data using a deep learning technique, obtaining driving colour maps which represent an immediate visualization with potential impacts on road safety

Psychological Analysis of Degree of Safety in Traffic Environment Design

See this work in the Center for Transportation Research Library catalog: https://library.ctr.utexas.edu/Presto/catalogid=5773Recent on-site accident investigation studies have estimated that between 10 and 25 percent of automobile accidents involve distraction as a principal causative factor. This report presents the findings of a research project designed to study the relationship between visual dis tractors in the roadside environment, such as advertising signs, neon lights, and gaudy billboards, and traffic safety. This project has involved the definition, operationalization, and measurement of visual distractibility in the traffic environment, including an analysis of distractions attributable to private signs and lights in the Vicinity of public signs and signals and of distractions caused by an overload or improper placement of public signs.Texas Office of Traffic SafetyCenter for Transportation ResearchSee this work in the Center for Transportation Research Library catalog

Computing driver tiredness and fatigue in automobile via eye tracking and body movements

The aim of this paper is to classify the driver tiredness and fatigue in automobile via eye tracking and body movements using deep learning based Convolutional Neural Network (CNN) algorithm. Vehicle driver face localization serves as one of the most widely used real-world applications in fields like toll control, traffic accident scene analysis, and suspected vehicle tracking. The research proposed a CNN classifier for simultaneously localizing the region of human face and eye positioning. The classifier, rather than bounding rectangles, gives bounding quadrilaterals, which gives a more precise indication for vehicle driver face localization. The adjusted regions are preprocessed to remove noise and passed to the CNN classifier for real time processing. The preprocessing of the face features extracts connected components, filters them by size, and groups them into face expressions. The employed CNN is the well-known technology for human face recognition. One we aim to extract the facial landmarks from the frames, we will then leverage classification models and deep learning based convolutional neural networks that predict the state of the driver as 'Alert' or 'Drowsy' for each of the frames extracted. The CNN model could predict the output state labels (Alert/Drowsy) for each frame, but we wanted to take care of sequential image frames as that is extremely important while predicting the state of an individual. The process completes, if all regions have a sufficiently high score or a fixed number of retries are exhausted. The output consists of the detected human face type, the list of regions including the extracted mouth and eyes with recognition reliability through CNN with an accuracy of 98.57% with 100 epochs of training and testing

Model-based estimation of the state of vehicle automation as derived from the driver’s spontaneous visual strategies

When manually steering a car, the driver’s visual perception of the driving scene and his or her motor actions to control the vehicle are closely linked. Since motor behaviour is no longer required in an automated vehicle, the sampling of the visual scene is affected. Autonomous driving typically results in less gaze being directed towards the road centre and a broader exploration of the driving scene, compared to manual driving. To examine the corollary of this situation, this study estimated the state of automation (manual or automated) on the basis of gaze behaviour. To do so, models based on partial least square regressions were computed by considering the gaze behaviour in multiple ways, using static indicators (percentage of time spent gazing at 13 areas of interests), dynamic indicators (transition matrices between areas) or both together. Analysis of the quality of predictions for the different models showed that the best result was obtained by considering both static and dynamic indicators. However, gaze dynamics played the most important role in distinguishing between manual and automated driving. This study may be relevant to the issue of driver monitoring in autonomous vehicles

The neural basis of hazard perception differences between novice and experienced drivers

The neural mechanisms underlying hazard perception are poorly understood as to how experience affects it in drivers. In this study we used functional magnetic resonance imaging (fMRI) to examine experienced-related changes in brain activation of a hazard perception skill between novice and aged drivers. Additionally, region of interest (ROI) and seed-to-voxel analysis were conducted to examine experienced-related functional connectivity changes in visual attention and saliency networks between novice (n=15, age 22.13± 3.38 years years) and experienced (n=16, age 41.44± 5.83 years) drivers. Experienced drivers had significantly lower hazard perception reaction time (1.32 ± 1.09 s) and miss rates (11.42 ± 8.36 %) compared to the novice (3.58± 1.45 s and 39.67 ± 15.72 %, respectively). Blood oxygen level dependent (BOLD) activation increased in occipital, parietal and frontal areas when executing hazard perception task in the both the groups. During task execution, experienced drivers showed, in general, greater activation in occipital lobe, Supramarginal Gyrus (SMG), right insular cortex, and anterior cingulate cortex (ACC) and cerebellar regions compared to the novice drivers indicating more efficient visual attention and decision-making processing in hazard perception skill. Seed based functional analyses in the task revealed greater connectivity between the ACC and the entire salience network (visual attention network) in the experienced group. Additionally, ACC had higher functional connectivity with right frontal eye field (FEF) and, bilateral Intraparietal Sulcus (IPS) and lateral occipital areas in the experienced group. Our results suggest that the hazard perception ability in experienced drivers is due to increase in the activation of executive attention regions, and better functional connectivity between bilateral occipital cortices and salience network. Better hazard perception performance is highly dependent on emotional awareness and attention to the velocity of motion