Roadside vegetation provides numerous environmental and psychological benefits to drivers. Previous studies have shown that natural landscapes can effectively lower crash rates and cause less stress and frustration to the driver. However, run-off-the-road crashes resulting in a collision with a tree are twice as likely to result in a fatality, thus reinforcing the need to examine the placement of vegetation within the clear zone. This study explores the relationship between the size of the clear zone and the presence of roadside vegetation on selected driver attributes, including both driver speed and lateral positioning. To evaluate the effect on the driver speed selection process, a static evaluation was employed. Completed by more than 100 drivers, the static evaluation was utilized to gather speed selections on both real and virtual roads containing four combinations of clear zone size and roadside vegetation density. Additionally, field data was collected to validate the findings of the static evaluation and to determine the extent to which roadside vegetation impacts driving attributes. When presented with a large clear zone, drivers positioned the vehicle further from the edge of the road as the vegetation density increased. Furthermore, the speeds observed in the field correlated with the speeds that participants selected when watching a video of the same road. Finally, the UMassSafe Traffic Safety Data Warehouse was utilized to link crash and roadway data, allowing for an in-depth analysis of run-off-the-road (ROR) crash severity. The results of this study further demonstrate the nature of the relationship between clear zone design and driver behavior.

Researchers at the University of Central Florida (UCF) have been working during the past few years on different strategies to improve freeway safety in real-time. An ongoing research at UCF has investigated crash patterns that occurred on a stretch of Interstate-4 located in Orlando, FL and created statistical models to predict in real-time the likelihood of a crash in terms of time and space. The models were then tested using PARAMICS micro-simulation and different strategies that would reduce the risk of crashes were suggested. One of the main recommended strategies was the use of Variable Speed Limits (VSL) which intervenes by reducing the speed upstream the segment of high risk and increasing the speed downstream. The purpose of this study is to examine the recommendations reached by the micro-simulation using the UCF driving simulator. Drivers' speed behavior in response to changes in speed limits and different information messages are observed. Different scenarios that represent the recommendations from the earlier micro-simulation study and three different messages displayed using Variable Message Signs (VMS) as an added measure to advice drivers about changes in the speed limit were created. In addition, abrupt and gradual changes in speed were tested against the scenarios that maintained the speed limit constant or did include a VSL or VMS in the scenarios' design (base case). Dynamic congestion was also added to the scenarios' design to observe drivers' reactions and speed reductions once drivers approached congestion. A total of 85 subjects were recruited. Gender and age were the controlling variables for the subjects' recruitment. Each of the subjects drove 3 out of a total of 24 scenarios. In addition, a survey was conducted and involved hypothetical questions, including knowledge about VMS and VSL, and questions about their driving behavior. The survey data were useful in identifying the subjects' compliance with the speed limit and VSL/VMS acceptance. Two statistical analytical techniques were performed on the data that were collected from the simulator: ANOVA and PROC MIXED. The ANOVA test was used to investigate if the differences in speed and reaction distances between subjects were statistically significant for each sign compared to the base case. The PROC MIXED analysis was used to investigate the differences of all scenarios (24x24) based on the spot speed data collected for each driver. It was found from the analyses that drivers follow better the message displayed on VMS that informs them that the speed is changing, whether it is or not, strictly enforced as opposed to providing the reason for change or no information. Moreover, an abrupt change in speed produced immediate results; however both abrupt and gradual changes in speed produced the same reduction in speed at the target zone. It was also noticed that most drivers usually drive 5 mph above the speed limit, even though in the survey analysis the majority of them stated that they drive in compliance with the speed limit or with the flow of traffic. This means that if a modest speed reduction of 5 mph is requested they will ignore it, but if a 10 mph reduction is recommended they will reduce the speed by at least 5 mph. Consequently, it was noticed that drivers arrived at the congestion zone with a slower speed than the base speed limit due to the combination of VMS and VSL signage. By having drivers approaching congestion with a slower speed, potential rear-end crashes could be avoided. Comparing the two genders indicated that females are more likely to follow the VMS's recommendations to reduce the speed. Also females in general drive above the speed limit between 2 mph and 3 mph, while males drive above the speed limit between 5 mph and 8 mph. From the analysis of the age factor, it was concluded that drivers from the 16-19 age group drive faster and drivers from the 45 and above age group drive slower, than the drivers from the other groups. In general, all drivers reduced and/or increased their speed accordingly when a VMS and/or VSL was present in the scenario advising for this change in the speed limit. The investigations conducted for this thesis proved that the recommendations suggested previously based on the crash risk model and micro-simulation (Abdel-Aty et al., 2006) aid drivers in reducing their speed before they approach a segment of high risk and by doing so reduce the likelihood of a crash. Finally, the real-time safety benefits of VMS and VSL should be continuously evaluated in future studies.

The accurate modeling of travelers’ route choice decision making when faced with unreliable (risky) travel times is necessary for the assessment of policies aimed at improving travel time reliability. Two major objectives are studied in this thesis. The first objective is to evaluate the applicability of a process model to route choice under risk where the actual process of decision making is captured. Traditionally, we adopt “as-if” econometric models to predict people’s route choice decisions. The second objective is to investigate travelers’ capability to incorporate future real-time traffic information into their current route choice decision making. Two separate stated preference (SP) surveys were conducted for each objective. The first SP survey used an interactive map in a computer based test. The second SP survey used a full-scale high-fidelity driving simulator. Compared with econometric models, process models have been rarely investigated in travel decision making under risk. A process model aims to describe the actual decision making procedure and could potentially provide a better explanation to route choice behavior. A process model, Priority Heuristic (PH), developed by Brandstatter et al. (2006) is introduced to the travel choice context and its probabilistic version, Probabilistic Priority Heuristic (PPH), is developed and estimated in this study. With data collected from a stated preference (SP) survey which is based on an animated computer interface, one econometric model, Rank-Dependent Expected Utility (RDEU) model, and two other alternative models were compared with the PPH model in a cross validation test to investigate their data-fitting and predictive performance. Our results show that the PPH model outperforms the RDEU model in both data-fitting and predictive performance. This suggests that the process modeling paradigm could be a promising new area in travel behavior research. With the advance of information and telecommunication technology, real-time traffic information is increasingly more available to help travelers make informed route choice decisions when faced with unreliable travel times. A strategic route choice refers to a decision taking into account future diversion possibilities at downstream nodes based on real-time information not yet available at the time of decision-making. Based on the data collected from a driving simulator experiment and a matching PCbased experiment, a mixed Logit model with two latent classes, strategic and nonstrategic route choice, is specified and estimated. The estimates of the latent class probabilities show that a significant portion of route choice decisions are strategic and subjects can learn to make more strategic route choice as they have more experience with the decision scenarios. Non-parametric tests additionally show that network complexity adversely affects travelers’ strategic thinking ability in a driving simulator environment but not in a PC environment and a parallel driving task only affects strategic thinking ability in a difficult scenario but not a simple one. In addition, we find that people’s strategic thinking ability are influenced by their gender and driving experience (mileage) in the non-parametric analysis, but not in the modeling work. These findings suggest that a realistic route choice model with real-time traffic information should consider both strategic and non-strategic behavior, which vary with the characteristics of both the network and the driver.

Emergency vehicles are at increased traffic risk due to legal exemptions like speeding or running red lights during emergencies. These exemptions can cause delays and complications in their response. The study explored if warning messages to passenger car drivers, particularly the EVA (Emergency Vehicle Approaching) message, with or without ambient lighting, could improve safety and response. A driving simulator experiment with 60 participants tested the impact of different levels of warning, including the EVA message and augmented ambient lighting (AEL). Participants also completed pre- and post-experiment questionnaires. The test involved a rural road scenario with background music to challenge emergency vehicle detection. Results showed significant behavioural differences between drivers receiving no warning and those receiving either form of warning, but no significant difference between the two warned groups. While attitudes towards this technology were positive, further research on the effectiveness of ambient lighting is needed. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

The studies reported in this thesis aim to provide insights on the process of navigation while driving. Driving requires processing and monitoring multiple tasks and sources of information. Navigation while driving increases the cognitive load of the driving task. Offloading the task of navigation to navigation aid systems such as GPS has potential disadvantages for our spatial memory skills. In this thesis, we introduce useful cues and skills to improve the performance of drivers in a variety of situations where they must navigate without the help of GPS. We used a motion simulator with six degrees of freedom to simulate various virtual reality driving scenarios that combine both visual and vestibular cues. In the following chapters, we report the effects of landmark cues, vestibular cues, self-reported mood disorders (e.g., depression, anxiety, and stress), individual differences at the visual spatial level (e.g., working memory span and mental rotation skills), age, and self-reported navigation skills on drivers’ route learning. We showed that successful navigation in various navigational situations depends on the type of landmarks available in the environment and the specific visual-spatial skills of drivers. We showed that vestibular self-motion cues improve egocentric route learning. Depression, anxiety, and stress affected drivers' route learning ability and dependency on GPS. We observed no deficit in age-related navigation performance when older drivers were able to use an egocentric frame of reference, however there was less optimal navigation performance of older drivers when wayfinding required an allocentric frame of reference. Overall, the application of the findings of this thesis may lead to an increase in efficacy and success in navigation performance and wayfinding while driving. / Thesis / Candidate in Philosophy / This thesis focuses on enhancing our understanding of wayfinding while driving in young and older adults. Using a driving simulator, we ran various virtual reality experiments to examine the underlying mechanisms of navigation while driving and ways to improve wayfinding of drivers. We identified useful cues for route learning in different environments where there were no navigation aid systems. We examined correlations between various spatial skills and performance that may improve drivers' wayfinding in unfamiliar environments. Furthermore, we assessed age-related effects on route learning and potential interventions to improve navigation in older drivers. The findings from the experiments reported in this thesis introduce the principle of route learning while driving in terms of how various internal and external factors can affect it. Drivers can incorporate these findings into their navigation tasks to overcome the wayfinding challenges that they encounter when driving in unfamiliar environments.

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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>

As the autonomous vehicles are getting clo-ser to commercial roll out, the challenges for the developersof the software are getting more complex. One challenge thedevelopers are facing is the interaction between humans andautonomous vehicles in traffic.Such situation requires a hugeamount of data to in order to design and proof test autonomoussystem than can handle complex interactions with humans.Such data can not be collected in real traffic situations withoutcompromising the safety of the human counterparts, thereforesimulations will be necessary. Since human driving behavior ishard to predict, these simulations need human interaction inorder to get valid data of human behaviour.The purpose of thisproject is to develop a driving interface and then evaluate theusers experience in an experiment. To do this we have designedand implemented steering,braking and acceleration on a userinterface for a simulator used in autonomous driving researchcalled Car Learning to Act (CARLA) at the Smart Mobility Lab(SML) at KTH. We have implemented two driving simulatoruser interfaces, with different levels of information feedbackto the user. To evaluate the developed user interface, a surveywas designed to measure how intuitive the driving experiencewas while also comparing it to the original setup at SML. Thesurvey showed that the driving experience was more intuitivewith the two developed user interfaces and that 60% would feelcomfortable using the new systems on a real vehicle in traffic. / Allteftersom autonoma bilar kommer närmare kommersiell lansering blir utmaningarna för utvecklarna av mjukvaran mer komplexa. En utmaning som utvecklarna står inför är interaktionen mellan autonoma bilar och människor i och utanför trafiken. Dessa situationer kommer kräva en stor mängd data för att säkerhetställa att autonoma bilar kommer kunna agera optimalt. För att inhämta sådan data utan att riskera säkerheten för alla ute i trafiken kommer simulatorer behövas. Eftersom vi inte kan förutspå mänskligt beteende kommer industrin behöva använda mänskliga förare i dessa simulatorer för att få realistiska resultat. Syftet med detta projekt är att utveckla ett förargränssnitt för människor och sedan utvärdera autenticiten av upplevelsen från ett mänskligt perspektiv. Genom att implementera olika bilmekanismer så som styrning, inbromsning, accelerationen och retardation i en simulator för autonom bil forskning, Car Learning To Act(CARLA) i Smart Mobility Lab(SML) på KTH. Vi implementerade två användargränssnitt med olika nivåer av informations återkoppling till användaren. För att utvärdera användargränssnitten utformades ett frågeformulär för att mäta hur intuitivt körupplevelsen var och samtidigt jämföra med det originella användargränssnittet i SML. Undersökningen visade att körupplevelsen var mer intuitiv med det två utvecklade användargränssnitten och att 60% skulle vara bekväma med att använda ett utav dessa system för att styra ett riktigt fordon i trafik. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

Vehicle manufacturers strive for an increasingly efficient and faster development process. Although computer-aided engineering has made significant progress toward a fully virtual development process, a challenge remains in integrating human subjective feedback to fully close the virtual development loop. Subjective assessment of ride and driving characteristics are still very important traits of a passenger car. Moving-base driving simulators have the ability to introduce the human into the virtual development loop, thus enabling subjective assessment of virtual vehicle models. Such an introduction has the potential to significantly speed up the development process and at the same time save resources by avoiding physical testing and providing informed decisions in the early phase of vehicle development cycles. The challenge to do so lies in the possibility to evaluate a vehicle in a driving simulator, which is highly dependent on the motion cueing. Motion cueing algorithms are used to map the vehicle motion into the confined workspace of a driving simulator. As of today, these algorithms are still often tuned and evaluated subjectively. The challenge with this approach is that it does not guarantee the fidelity of the cueing and it needs physical vehicles to be compared with. This work thus focuses on the objective development and evaluation of motion cueing, which potentially could enable high fidelity motion cueing in the early stages of the vehicle development process, when prototypes are not available. This is very important for winter testing since the testing is challenging with regards to ambient conditions, the limited testing season and the increasing need to speed up the development process. The goal of this work is to move towards an objective approach to cueing evaluation based on physical models combining vehicle model, simulator, and human. Therefore, this thesis presents an objective methodology to motion cueing evaluation and development. Based on the state-of-the-art review, this work addresses the need for simple linear models to evaluate the fidelity of motion cueing algorithms. The linear model is applied to the problem of positioning the longitudinal axis of rotation of the simulator cabin and shows promising results when compared to time series-based optimisation and subjective assessment. Furthermore, using the same model to improve the motion cueing by introducing tilt coordination shows that even though the immersion is improved, the tilt coordination changes the perceived vehicle characteristics. To objectively evaluate different yaw cueing strategies in winter conditions, a more detailed human model is introduced that extends the state-of-the-art vestibular organ models by introducing gaze stabilisation using a model of the vestibulo-collic reflex. The cueing evaluation indicates the potential of separating slip angle feedback from the high-pass filtering of motion cues, as well as the advantage of using the vehicle’s motion as a target for cueing optimisation rather than the human vestibular response in winter handling evaluation. By addressing the inherent skewing of vehicle characteristics in motion cueing and suggesting improvements to the evaluation and cueing strategies, this work contributes to the possibility of virtually evaluating the vehicle dynamic characteristics in driving simulators under winter conditions.