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The aim of the thesis was to investigate if geofence instructions communicated via an in-vehicle human-machine interface (HMI) can have a positive impact on driver behavior when interacting with emergency vehicles. A total of n = 64 study participants were tested in a driving simulator on two different use cases without or with applied geofence instructions. The use cases were situated on an off-ramp and at an intersection. The results of the experiment demonstrated a statistically significant effect of the use of geofencing on the correct and timely reactions of drivers prior to the interaction with emergency vehicles. Furthermore, the use of geofencing indicated a potential to decrease collision risks and driving time of emergency vehicles. Although the HMI design needs to be improved for real-world geofence application, the study participants were positive about receiving the geofence instructions when interacting with emergency vehicles in their own vehicles in the future. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

A potential factor for curve accidents are anticipatory skills. Horizontal curves have been recognized as a significant safety issue for many years. This study investigates the impact and effectiveness of three curve based perceptual speed calming countermeasures (advanced curve warning signs, chevron sign, and heads-up display(HUD) sign) on drivers’ hazard anticipation and mitigation behavior across both left and right-winding curves, and sharp (radius 200m) and flat (radius 500m) curves. Experimental results show that the speed and lateral control in the horizontal curves differed with respect to curve radii, direction, and the type of countermeasure presented. These differences in behavior are probably due to curve-related disparities, the type of perceptual countermeasure, and the presence of hazard at the apex of the curve. HUD is found to be effective at not only reducing the drivers’ speed in the curve, but also improve the latent hazard anticipation ability of the driver at the apex of the curve. Flat and sharp curves with indications of a safety problem were virtually developed in the simulator as representative as possible without upsetting the simulator’s fidelity. 48 participants were recruited for this study between the age range of 18 and 34, and driving experience range was from 0.25 to 17.75 years.

Vehicle recognition and trajectory tracking plays an integral role in many aspects of Intelligent Transportation Systems (ITS) applications; from behavioral modeling and car-following analyses to congestion prevention, crash prediction, dynamic signal timing, and active traffic management. This dissertation aims to improve the tasks of multi-object detection and tracking (MOT) as it pertains to urban traffic by utilizing the domain knowledge of traffic flow then utilize this improvement for applications in real-time traffic performance assessment, safety evaluation, and driver behavior modeling. First, the author proposes an ad-hoc framework for real-time turn count and trajectory reconstruction for vehicles passing through urban intersections. This framework introduces the concept of virtual traffic lanes representing the eight standard National Electrical Manufacturers Association (NEMA) movements within an intersection as spatio-temporal clusters utilized for movement classification and vehicle re-identification. The proposed framework runs as an additional layer to any multi-object tracker with minimal additional computation. The results obtained for a case study and on the AI City benchmark dataset indicate the high ability of the proposed framework in obtaining reliable turn count, speed estimates, and efficiently resolving the vehicle identity switches which occur within the intersection due to detection errors and occlusion. The author then proposes the utilization of the high accuracy and granularity trajectories obtained from video inference to develop a real-time safety-based driver behavior model, which managed to effectively capture the observed driving behavior in the site of study. Finally, the developed model was implemented as an external driver model in VISSIM and managed to reproduce the observed behavior and safety conflicts in simulation, providing an effective decision-support tool to identify appropriate safety interventions that would mitigate those conflicts. The work presented in this dissertation provides an efficient end-to-end framework and blueprint for trajectory extraction from road-side traffic video data, driver behavior modeling, and their applications for real-time traffic performance and safety assessment, as well as improved modeling of safety interventions via microscopic simulation. / Doctor of Philosophy / Traffic crashes are one of the leading causes of death in the world, averaging over 3,000 deaths per day according to the World Health Organization. In the United States alone, there are around 40,000 traffic fatalities annually. Approximately, 21.5% of all traffic fatalities occur due to intersection-related crashes. Intelligent Transportation Systems (ITS) is a field of traffic engineering that aims to transform traffic systems to make safer, more coordinated, and 'smarter' use of transport networks. Vehicle recognition and trajectory tracking, the process of identifying a specific vehicle's movement through time and space, plays an integral role in many aspects of ITS applications; from understanding how people drive and modeling that behavior, to congestion prevention, on-board crash avoidance systems, adaptive signal timing, and active traffic management. This dissertation aims to bridge the gaps in the application of ITS, computer vision, and traffic flow theory and create tools that will aid in evaluating and proactively addressing traffic safety concerns at urban intersections. The author presents an efficient, real-time framework for extracting reliable vehicle trajectories from roadside cameras, then proposes a safety-based driving behavior model that succeeds in capturing the observed driving behavior. This work is concluded by implementing this model in simulation software to replicate the existing safety concerns for an area of study, allowing practitioners to accurately model the existing safety conflicts and evaluate the different operation and safety interventions that would best mitigate them to proactively prevent crashes.

Driving field experiment data were used to investigate lane changes in which a slow lead vehicle was present to: 1) characterize lane changes, 2) develop predictive models, 3) provide collision avoidance system (CAS) design guidelines. A total of 3,227 slow lead vehicle lane changes over 23,949 miles were completed by sixteen commuters. Two instrumented vehicles, a sedan and an SUV, were outfitted with video, sensor, and radar data systems that collected data in an unobtrusive manner. Results indicate that 37.2% of lane changes are slow lead vehicle lane changes, with a mean completion time of 6.3 s; most slow lead vehicle lane changes are leftward, rated low in urgency and severity. A stratified sample of 120 lane changes was selected to include a range of maneuvers. On the interstate, lane changes are performed less often, <i>t</i>(30) = 2.83, <i>p</i> = 0.008, with lower urgency ratings, <i>F</i>(1, 31) = 5.24, <i>p</i> = 0.05, as compared to highway lane changes, as interstates are designed for smooth flow. Drivers who usually drive sedans are more likely to make lane changes than drivers of SUVs, <i>X</i> ²⁺(1)= 99.6247, <i>p</i> < 0.0001, suggesting that driving style is maintained regardless of which experimental vehicle is driven. Turn signals are used 64% of the time but some drivers signal after the lane change starts. Of cases in which signals are not used, 70% of them are made with other vehicles nearby. Eyeglance analysis revealed that the forward view, rearview mirror, and left mirror are the most likely glance locations. There are also distinct eyeglance patterns for lane changing and baseline driving. Recommendations are to use forward view or mirror-based visual displays to indicate presence detection, and auditory displays for imminent warnings. The "vehicle + signal" logistic regression model is best overall since it takes advantage of the distance to the front and rear adjacent vehicle, forward time-to-collision (TTC), and turn signal activation. The use of additional regressors would also improve the model. Five design guidelines are included to aid in the development of CAS that are useable, safe, and integrated with other systems, given testing and development. / Ph. D.

The onset of a yellow indication is typically associated with the risk of vehicle crashes resulting from dilemma-zone and red-light-running problems. Such risk of vehicle crashes is greater for high-speed signalized intersection approaches. The research presented in this dissertation develops statistical as well as behavioral frameworks for modeling driver behavior while approaching high-speed signalized intersection approaches at the onset of a yellow indication. The analysis in this dissertation utilizes two sources of data. The main source is a new dataset that was collected as part of this research effort during the summer of 2008. This experiment includes two instructed speeds; 72.4 km/h (45 mph) with 1727 approaching trials (687 running and 1040 stopping), and 88.5 km/h (55 mph) with 1727 approaching trials (625 running and 1102 stopping). The complementary source is an existing dataset that was collected earlier in the spring of 2005 on the Virginia Smart Road facility. This dataset includes a total of 1186 yellow approaching trials (441 running and 745 stopping). The adopted analysis approach comprises four major parts that fulfill the objectives of this dissertation. The first part is concerned with the characterization of different driver behavioral attributes, including driver yellow/red light running behavior, driver stop-run decisions, driver perception-reaction times (PRT), and driver deceleration levels. The characterization of these attributes involves analysis of variance (ANOVA) and frequency distribution analyses, as well as the calibration of statistical models. The second part of the dissertation introduces a novel approach for computing the clearance interval duration that explicitly accounts for the reliability of the design (probability that drivers do not encounter a dilemma zone). Lookup tables are developed to assist practitioners in the design of yellow timings that reflects the stochastic nature of driver PRT and deceleration levels. An extension of the proposed approach is presented that can be integrated with the IntelliDriveSM initiative. Furthermore, the third part of the dissertation develops an agent-based Bayesian statistics approach to capture the stochastic nature of the driver stop-run decision. The Bayesian model parameters are calibrated using the Markov Chain Monte Carlo (MCMC) slice procedure implemented within the MATLAB® software. In addition, two procedures for the Bayesian model application are illustrated; namely Cascaded regression and Cholesky decomposition. Both procedures are demonstrated to produce replications that are consistent with the Bayesian model realizations, and capture the parameter correlations without the need to store the set of parameter realizations. The proposed Bayesian approach is ideal for modeling multi-agent systems in which each agent has its own unique set of parameters. Finally, the fourth part of the dissertation introduces and validates a state-of-the-art behavioral modeling framework that can be used as a tool to simulate driver behavior after the onset of a yellow indication until he/she reaches the intersection stop line. The behavioral model is able to track dilemma zone drivers and update the information available to them every time step until they reach a final decision. It is anticipated that this behavioral model will be implemented in microscopic traffic simulation software to enhance the modeling of driver behavior as they approach signalized intersections. / Ph. D.

Forward Collision Avoidance Systems (FCAS) are a type of active safety system which have great potential for rear-end collision avoidance. These systems use either radar, lidar, or cameras to track objects in front of the vehicle. In the event of an imminent collision, the system will warn the driver, and, in some cases, can autonomously brake to avoid a crash. However, driver acceptance of the systems is paramount to the effectiveness of a FCAS system. Ideally, FCAS should only deliver an alert or intervene at the last possible moment to avoid nuisance alarms, and potentially have drivers disable the system. A better understanding of normal driving behavior can help designers predict when drivers would normally take avoidance action in different situations, and customize the timing of FCAS interventions accordingly. The overall research object of this dissertation was to characterize normal driver behavior in car following events based on naturalistic driving data. The dissertation analyzed normal driver behavior in car-following during both braking and lane change maneuvers. This study was based on the analysis of data collected in the Virginia Tech Transportation Institute 100-Car Naturalistic Driving Study which involved over 100 drivers operating instrumented vehicles in over 43,000 trips and 1.1 million miles of driving. Time to Collision in both braking and lane change were quantified as a function of vehicle speed and driver characteristics. In general, drivers were found to brake and change lanes more cautiously with increasing vehicle speed. Driver age and gender were found to have significant influence on both time to collision and maximum deceleration during braking. Drivers age 31-50 had a mean braking deceleration approximately 0.03 g greater than that of novice drivers (age 18-20), and female drivers had a marginal increase in mean braking deceleration as compared to male drivers. Lane change maneuvers were less frequent than braking maneuvers. Driver-specific models of TTC at braking and lane change were found to be well characterized by the Generalized Extreme Value distribution. Lastly, driver's intent to change lanes can be predicted using a bivariate normal distribution, characterizing the vehicle's distance to lane boundary and the lateral velocity of the vehicle. This dissertation presents the first large scale study of its kind, based on naturalistic driving data to report driver behavior during various car-following events. The overall goal of this dissertation is to provide a better understanding of driver behavior in normal driving conditions, which can benefit automakers who seek to improve FCAS effectiveness, as well as regulatory agencies seeking to improve FCAS vehicle tests. / Ph. D.

The driver-specific data from a naturalistic driving study provides car-following events in real-world driving situations, while additionally providing a wealth of information about the participating drivers. Reducing a naturalistic database into finite car-following events requires significant data reduction, validation, and calibration, often using manual procedures. The data collection performed herein included: the identification of commuting routes used by multiple drivers, the extraction of data along those routes, the identification of potential car-following events from the dataset, the visual validation of each car-following event, and the extraction of pertinent information from the database during each event identified. This thesis applies the developed process to generate car-following events from the 100-Car Study database, and applies the dataset to analyze four car-following models. The Gipps model was found to perform best for drivers with greater amounts of data in congested driving conditions, while the Rakha-Pasumarthy-Adjerid (RPA) model was best for drivers in uncongested conditions. The Gipps model was found to generate the lowest error value in aggregate, with the RPA model error 21 percent greater, and the Gaxis-Herman-Rothery model (GHR) and the Intelligent Driver Model (IDM) errors 143 percent and 86 percent greater, respectively. Additionally, the RPA model provides the flexibility for a driver to change vehicles without the need to recalibrate parameter values for that driver, and can also capture changes in roadway surface type and condition. With the error values close between the RPA and Gipps models, the additional advantages of the RPA model make it the recommended choice for simulation. / Master of Science

Esta pesquisa caracterizou o comportamento de motoristas em interseções semaforizadas sob três aspectos: (1) reação frente à mudança do verde para o amarelo; (2) comportamento durante a desaceleração para parar; e (3) comportamento durante a saída do cruzamento semaforizado. Os dados foram coletados em uma interseção localizada em pista de testes no Virginia Tech Transportation Institute, nos EUA. A amostra foi composta por 60 motoristas voluntários igualmente divididos em função do gênero; dos quais 32 tinham idade inferior a 65 anos (\"jovens\"). Foram investigados efeitos da idade, do gênero e da declividade da via sobre as seguintes situações: tomada de decisão entre parar ou prosseguir no amarelo; posição de parada em relação à faixa de retenção; tempo de percepção e reação (TPR) para frenagem e partida do cruzamento; efeito de zonas de opção e de dilema; taxa de desaceleração para parada na interseção; e taxa de aceleração para partida da interseção. As análises indicaram que: (1) os motoristas mais jovens invadiram mais a faixa de retenção que os idosos; (2) mulheres apresentam maiores TPR para decidir partir da interseção; e (3) o TPR é menor no declive tanto para a decisão de frear quanto para a partida do cruzamento. As taxas de desaceleração não apresentaram influência dos fatores avaliados. Por outro lado, constatou-se que a aceleração foi afetada pelo fator declividade. Como resultado final da pesquisa, foram propostos modelos, em função do tempo, que exprimem a desaceleração/aceleração usada pelos motoristas ao frear/acelerar. Foram propostos modelos para o motorista médio e para motoristas desagregados em três grupos em função da agressividade. / The objective of this research was to characterize driver behavior at signalized intersections according to three aspects: (1) reaction at the onset of the amber phase; (2) behavior during the deceleration to stop at the signal; and (3) behavior during the acceleration to leave the intersection at the onset of the green. The data were collected at a signalized intersection on a private highway, at the Virginia Tech Transportation Institute, in the USA. The sample consisted of 60 volunteer drivers, equally divided by gender. The sample was divided into two age groups: younger drivers (age was less than 65) and older drivers. Effects of gender, age group and roadway grade were investigated for the following aspects: decision making at the onset of amber; final stopping position with relation to the stop line; perception/reaction times (PRT) at the onset of the amber and the green lights; effects of dilemma and option zones; and deceleration and acceleration rates used by the drivers. The analyses suggest that: (1) younger drivers tend to stop farther past the stop line, compared to older drivers; (2) women have longer PRT at the onset of the green; and (3) PRT are shorter on downgrade at the onset of both amber and green lights. The observed deceleration rates were not affected by gender, age group or roadway grade. Acceleration rates were found to be influenced by the grade. A set of models that express the acceleration/deceleration rates as a function of time were proposed to represent the average behavior observed for drivers in the sample. Specific models were also proposed for aggressive, non-aggressive and intermediate drivers.

<p>In this thesis report we conducted research study on driver's behavior in T-Intersections using simulated environment. This report describes and discusses correlation analysis of driver's personality traits and style while driving at T-Intersections.</p><p>The experiments were performed on multi user driving simulator under controlled settings, at Linköping University. A total of forty-eight people participated in the study and were divided into groups of four, all driving in the same simulated world.</p><p>During the experiments participants were asked to fill a series of well-known self-report questionnaires. We evaluated questionnaires to get the insight in driver's personality traits and driving style. The self-report questionnaires consist of Schwartz's configural model of 10 values types and NEO-five factor inventory. Also driver's behavior was studied with the help of questionnaires based on driver's behavior, style, conflict avoidance, time horizon and tolerance of uncertainty. Then these 10 Schwartz's values are correlated with the other questionnaires to give the detail insight of the driving habits and personality traits of the drivers.</p>