abstract: Research on priming has shown that exposure to the concept of fast food can have an effect on human behavior by inducing haste and impatience (Zhong & E. DeVoe, 2010). This research suggests that thinking about fast food makes individuals impatient and strengthens their desire to complete tasks such as reading and decision making as quickly and efficiently as possible. Two experiments were conducted in which the effects of fast food priming were examined using a driving simulator. The experiments examined whether fast food primes can induce impatient driving. In experiment 1, 30 adult drivers drove a course in a driving simulator after being exposed to images by rating aesthetics of four different logos. Experiment 1 did not yield faster driving speeds nor an impatient and faster break at the yellow light in the fast food logo prime condition. In experiment 2, 30 adult drivers drove the same course from experiment 1. Participants did not rate logos on their aesthetics prior to the drive, instead billboards were included in the simulation that had either fast food or diner logos. Experiment 2 did not yielded faster driving speeds, however there was a significant effect of faster breaking and a higher number of participants running the yellow light. / Dissertation/Thesis / M.S. Applied Psychology 2014

This Master thesis report is the research conducted at the Linköping University (LiU) in the Cognitive Engineering group. This report describes and discusses the possible driving variations at T-intersections. In this study we tested how voice based command (GPS) system and traffic lights did influence the driving behavior. This computational study was conducted on a multi user driving simulation environment at Linköping University. A total of 12 groups each consisting of 4 persons participated in this study. The participants also completed a survey on paper with their valuable comments. To study the driving behavior we analyzed the conflict indicators at the Tintersection. We selected Post Encroachment Time (PET), speed and acceleration as good conflict indicators.

Realism is a very sought feature in interactive driving simulators for traffic studies, since a nonrealistic simulation could produce non-realistic human drivers behaviours. Since cars driven by artificial intelligence (AI) are one of the main components of a traffic simulation, they play an important role in making up the overall sense of realism. A good understanding of how the AI influences human drivers is thus important in avoiding biases in traffic studies with simulators, and might also come useful in simulators for traffic education, in order to induce certain behaviours in the students. The purpose of this study was to build a driving simulation with multiple AI-driven cars, and let human testers use it, in order to analyse if and how a more polite lane-change behaviour, a more realistic lateral alignment, and a slower average speed affect the perception and the behaviour of human drivers. The simulator was developed upon low-cost hardware infrastructure previously used for other traffic studies. Since the existing software is very specific and hard to modify, a new simulation software was built from scratch for this study, using the Unity3D engine and implementing design patterns developed in previous studies, in order to produce a more flexible and modifiable infrastructure than what had been done in the past studies. The test subjects gave a generally good feedback on the simulator as a whole, and cars which politely changed lanes were regarded as behaving in a slightly more realistic way. Some insights were also obtained about user perception, mainly consisting in a difficulty in perceiving absolute speeds, whereas relative speeds were estimated more accurately.

The urban population is growing at an exponential rate throughout the world leading to the problems related to swift and speedy mobility or issues caused by convectional mobility options. This study illustrates and explores the new ways to transport people specially taking into account the self-driving cars concept and discusses the concept of mobility 4.0 (smart / intelligent mobility) and briefly highlights the technological aspects of autonomous vehicles, adaptation advantages and progress in laws and legislations of autonomous vehicle. The study is primarily qualitative and relies on the work of previous researcher, technical reports and blogs but the part of this study is quantitative where empirical data was collected from the experts in a conference held by BBG Austria. The result of the studies shows adaptation readiness of Austrian professional market and business prospects associated with autonomous vehicles Moreover, different business models are suggested, which could be adopted to incorporate the driverless vehicles in day-to-day life of an individual living in urban environment. The models basically suggest that the adaptation of the technology would help curbing transport externalities especially external cost associated to transportation of each individual; which includes congestion, accident, infrastructure costs and environmental costs which are incurred by least efficient conventional cars and would also help shrinking the diseases like premature mortality, aggravation of respiratory as well as cardiovascular disease and sleep disturbance which are the result of city level congestion and pollution. (author's abstract) / Series: Schriftenreihe des Instituts für Transportwirtschaft und Logistik - Verkehr

Background: Nowadays, logistics service providers face several challenges which create an urge to rethink their strategy to improve their position within the market,decrease their costs and their environmental impact. At the same time theintroduction of autonomous driving potentially has an impact on logistics.Self-driving trucks can help logistics companies to tackle these challenges.However, the implementation of this technology could fundamentally alterthe competitive landscape. Hence, certain competitive advantages currentlyheld by logistics firms might lose their relevance in the future and need tobe adapted to maintain a strong market position. Purpose: The purpose of this study is to explore the perception of self-driving trucks within logistics and the impact on competitive advantages of logistics service providers. Thereby, this thesis will look at experts from Germany and Sweden and their opinion on future implications of self-driving trucks. Method: An inductive research approach is used to explore the topic. A multi-method research strategy is applied to gather data through qualitative semi-structured interviews with 17 participants. These were divided into five different case groups. To interpret the data a thematic analysis approach was chosen. Conclusion: The main contribution is a model representing the impact of autonomous driving on competitive advantages and the implications for the logistics industry. Findings are based on the perception of experts about autonomous driving, current resources and capabilities.

With more legislation being enforced to achieve a reduction in road transport CO2 emissions, automotive companies are having to research and develop technologies that deliver greener driving . Whilst emissions from passenger vehicles have dropped over recent years, there has been an increase in emissions from light commercial vehicles (LCVs). The nature of LCV delivery work is a routine of ingress/egress of the vehicle, changing from a standing to a seated posture repetitively throughout the day. One research focus is packaging occupants in to a smaller vehicle space, in order to reduce the amount of vehicle emissions over its lifecycle. For LCVs, benefits from space saving technology could be an increase in overall loading space (with the same vehicle length) or a reduction in the overall length/weight of the vehicle. Furthermore, an elevated seat posture could reduce the strain on drivers during ingress/egress, as it is closer than that of a conventional seat to a standing posture. Whilst space saving technology has obvious benefits, current driving conventions and standards are not inclusive of new and novel seated postures when packaging a driver in to a vehicle. The fundamental purpose of a vehicle driver s seat is to be comfortable and safe for the occupant and to facilitate driving. It has been shown that a seat needs both good static and dynamic factors to contribute to overall seat comfort. Additionally, comfortable body angles have been identified and ratified by studies investigating comfortable driving postures; however, this knowledge only applies to conventional driving postures. For an elevated posture , defined as having the driver s knee point below the hip point, there is little research or guidance. The overall aim of this thesis is to identify the ergonomic requirements of a wide anthropometric range of drivers in an elevated driving posture for LCVs, which was investigated using a series of laboratory based experiments. An iterative fitting trial was designed to identify key seat parameters for static comfort in an elevated posture seat. The results showed that in comparison with a conventional seat: Seat base length was preferred to be shorter (380mm compared with 460mm); Seat base width was preferred to be wider (560mm compared with 480mm); Backrest height was preferred to be longer (690mm compared with 650mm). These findings provided a basis for a seat design specification for an elevated posture concept seat, which was tested in two subsequent laboratory studies. A long-term discomfort evaluation was conducted, using a driving simulator and a motion platform replicating real road vibration. Discomfort scores were collected at 10-minute intervals (50-minutes overall) using a body map and rating scale combination. The results indicated that in comparison with the conventional posture, the elevated posture performed as well, or better (significantly lower discomfort for right shoulder and lower back; p<0.05, two-tailed), in terms of long-term discomfort. Furthermore, the onset of discomfort (i.e. the time taken for localised discomfort ratings to be significantly higher than the baseline ratings reported before the trial) occurred after as little as 10 minutes (conventional posture) and 20 minutes (elevated posture) respectively. A lateral stability evaluation was conducted using low-frequency lateral motion on a motion platform (platform left and right rolls of 14.5°). Stability scores were reported after each sequence of rolls, comparing scores on a newly developed lateral stability scale between three seats: Conventional posture seat; Elevated posture concept seat (EPS1); Elevated posture concept seat with modifications aimed at improving stability (EPS2). Participants reported being more unstable in EPS1, compared with the conventional posture seat (p<0.05, Wilcoxon). However, the EPS2 seat performed equally to the conventional posture seat. These findings suggest that the elevated posture seat developed in this research is a feasible and comfortable alternative to a conventional posture seat. Furthermore, the final elevated seating positions showed that real space saving can be achieved in this posture thus allowing for more compact and lighter vehicles and potentially reducing strain on drivers during ingress/egress.

The future of mobility is changing at an exponential rate, as every day passes it moves closer to the goal of complete autonomy, therefore, it is safe to say that the adaptation of self-driving cars in near future is no more a matter of science fiction. Keeping in view the rapid evolution of mobility, this study tries to enlighten and compile the importance of autonomous vehicles in our daily life by highlighting monetary and societal advantages of adaptation. Moreover, it analysis the Austrian expert's opinion on the topic of adaptation of the self- driving cars and business prospects with autonomous vehicles for current businesses. The data on these two questions was collected in a workshop using Delphi method, where experts from varied professional backgrounds participated. But mainly the participants of the controlled group were representatives of public procurement, energy and automobile industry/sector. After a detailed presentation and discussion over the topic, the participants were asked for their views on forth mentioned two questions. Their opinions were recorded and visualized on a custom made graph and further analyzed using the descriptive statistical tool. At the end of the question and answer session a vast majority of experts, which is approximately 80%, thought that there is a good possibility of adaptation of autonomous vehicles in near future. But on the other hand relatively less percentage of experts were confident about the bright future for current businesses in automobile industry; They argued that the current business decorum would change dramatically in a couple of decades and this would be the question of survival of the fittest and smartest.

Over the last years, driving automation has increasingly moved into focus in human factors research. A large body of research focusses on situations in which the human driver needs to regain control. However, little research has so far been conducted on how SAE level 3+ automated driving should be designed with focus on occupant comfort. This thesis aims at identifying a comfortable driving style for automated vehicles. As a basis, it was necessary to pinpoint driving metrics, which vary between driving styles and can be manipulated in order to design a comfortable driving style. Hence, Study 1 was conducted, in which drivers (N = 24) manually drove on a highway or on urban and rural roads with certain driving styles. Results show relevant metrics (i.e., lateral and longitudinal acceleration, lateral and longitudinal jerk, quickness, and headway distance in seconds) and that these metrics vary across maneuvers and thus, a maneuver-specific analysis is recommended. As these metrics are derived from manual data, it remained unclear after Study 1, in which range the metric values should vary for comfortable automated driving. Therefore, as a second step, the main metrics were varied and the subsequent combinations implemented in an automated vehicle as well as in a dynamic simulator with two different configurations. The combinations were then subject to ratings by 72 participants. Results show that the metrics and values found in Study 1, are able to elicit a range of comfort ratings in automated driving. It was also found, that acceleration is a key variable in experiencing comfort. However, it is not the sole predictor. Additionally, as higher levels of automated driving with larger velocities are still bound to considerable constraints for on-road testing, the second study was also used to validate a dynamic driving simulator to allow comfort during automated driving to be studied. In comparison to ratings on a test track, the dynamic simulator setting with longitudinal orientation is able to show both relative and absolute validity of comfort ratings. In the third and final step, different approaches to automated maneuvers were rated by participants (N = 72) regarding the comfort they experienced. A lane change, an acceleration, and a deceleration maneuver were chosen as test maneuvers. The lateral or longitudinal acceleration was varied in each of these maneuvers. Results, again, show comfort ratings are maneuver specific. On one hand, symmetrical and early-onset lane change maneuvers and symmetrical acceleration maneuvers were preferred. However, symmetrical deceleration maneuvers and deceleration maneuvers with a slower acceleration decrease evoke the highest comfort ratings. These ratings made it possible to offer guidelines for the design of automated driving styles. Furthermore, dependence on a number of personality traits was analyzed. Results suggest the general preference for certain driving styles to be unaffected by personality. However, it seems, participants with certain personality types are less particular about their preference for certain driving styles. Summed up, comfortable automated driving is – under the investigated circumstances – characterized by maneuvers with sufficient headway distance and smooth applications of small acceleration and small jerk. These should, even so, still provide sufficient motion feedback. Surrounding traffic seems to play an important role through urgency and should be considered for on-road implementation. Differences in personality did not seem to play a crucial role.

Plug-in electric vehicles (PEV) can reduce greenhouse gas emissions. However, the utility of PEVs, as well as reduction of emissions is highly dependent on daily vehicle kilometres travelled (VKT). Further, the daily VKT by individual passenger cars vary strongly between days. A common method to analyse individual daily VKT is to fit distribution functions and to further analyse these fits. However, several distributions for individual daily VKT have been discussed in the literature without conclusive decision on the best distribution. Here we analyse three two-parameter distribution functions for the variation in daily VKT with four sets of travel data covering a total of 190,000 driving days and 9.5 million VKT. Specifically, we look at overall performance of the distributions on the data using four goodness of fit measures, as well as the consequence of choosing one distribution over the others for two common PEV applications: the days requiring adaptation for battery electric vehicles and the utility factor for plug-in hybrid electric vehicles. We find the Weibull distribution to fit most vehicles well but not all and at the same time yielding good predictions for PEV related attributes. Furthermore, the choice of distribution impacts PEV usage factors. Here, the Weibull distribution yields reliable estimates for electric vehicle applications whereas the log-normal distribution yields more conservative estimates for PEV usage factors. Our results help to guide the choice of distribution for a specific research question utilising driving data and provide a methodological advancement in the application of distribution functions to longitudinal driving data.

This degree project investigates the suitability of using Behavior Trees (BT) as an architecture for the behavioral layer in autonomous driving. BTs originate from video game development but have received attention in robotics research the past couple of years. This project also includes implementation of a simulated traffic environment using the Unity3D engine, where the use of BTs is evaluated and compared to an implementation using finite-state machines (FSM). After the initial implementation, the simulation along with the control architectures were extended with additional behaviors in four steps. The different versions were evaluated using software maintainability metrics (Cyclomatic complexity and Maintainability index) in order to extrapolate and reason about more complex implementations as would be required in a real autonomous vehicle. It is concluded that as the AI requirements scale and grow more complex, the BTs likely become substantially more maintainable than FSMs and hence may prove a viable alternative for autonomous driving.