Interpreting the subtleness and complexity of vulnerable road user (VRU) behaviour is still a significant challenge for automated vehicles (AVs). Solutions for facilitating safe and acceptable interactions in future automated traffic include equipping AVs and VRUs with human-machine interfaces (HMl.s), such as awareness and notification systems, and connecting road users to a network of A Vs and infrastructure. The research on these solutions, however, primarily focuses on pedestrians. There is no overview ofthe type of systems or solutions supporting cyclists in future automated traffic. The objective ofthe present study is to synthesise current literature and provide an overview ofthe state-ofthe-art support systems available to cyclists. The aim is to identify, classify, and count the types of communicative technologies, systems, and devices capable of supporting the safety of cyclists in automated traffic. The overall goal is to understand A V-cyclist interaction better, pinpoint knowledge gaps in current literature, and develop strategies for optimising safe and pleasant cycling in future traffic environments with AVs.

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Cycling as an active mode of transport is increasing across all Europe [1]. Multiple benefits are coming from cycling both for the single user and the society as a whole. With increasing cycling, we expect more conflicts to happen between cyclists and vehicles, as it is also shown by the increasing cyclists' share of fatalities, contrary to the passenger cars' share [2]. Understanding cyclists' behavioral patterns can help automated vehicles (AVs) to predict cyclist's behavior, and then behave safely and comfortably when they encounter them. As a result, developing reliable predictive models of cyclist behavior will help AVs to interact safely with cyclists.

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Traffic shockwaves are a regularly occurring phe-nomenon in traffic that are a source of irritation and delaysfor the road users. One type of shockwave is the stop-and-gowave which forces entering drivers to stop and advance slowlyuntil the wave is passed. This project aims to design a controlalgorithm through the use of models and simulations to increasethe rate at which a stop-and-go wave dissipates. To design themodel and algorithm the Simulation of Urban MObility (SUMO)simulator and the Traffic Control Interface (TraCI) were usedin conjunction with Python. The setup used for simulation wasthat of a one way, two lane highway with an artificially inducedstop-and-go wave.The designed algorithm manages to dissipate a stop-and-go wavecompletely without introducing new ones. / Trafikvågorär ett vanligt förekommandefenomen i trafiken vilketär en orsak till frustration ochförseningar. En typ av vågär startochstop vågen som tvingarförare att stanna och långsamt fortsätta genom vågen tills denpasserat. Målet med detta projektär att utveckla en kontrol-lalgoritm med hjälp av modeller och simuleringar för attökaavtagandet av en sådan våg. För att utveckla modellen ochalgoritmen används simulatorn Simulation of Urban MObility(SUMO) och Traffic Control Interface (TraCI) i kombinationmed programmeringsspråket Python. Simulering gjordes på ettnätverk bestående av en enkelriktad, tvåfilig motorväg med enkonstgjord startochstop våg.En algoritm utvecklades som kan skingra en startochstop vågutan att skapa nya. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

Driving behavior considerably affects the transportation system, especially lane-changing behavior occasionally cause conflicts between drivers and induce shock waves that propagate backward. A freeway merge section is one of locations observed a freeway bottleneck, generating freeway traffic congestion. The emerging technologies, such as autonomous vehicles (AVs) and vehicle connectivity, are expected to bring about improvement in mobility, safety, and environment. Hence the objective of this study is to enhance freeway merge section operations based on the advanced technologies. To achieve the objective, this study modeled the non-cooperative merging behavior, and then proposed the cooperative applications in consideration of a connected and automated vehicles (CAVs) environment. As a tactical process, decision-making for lane-changing behaviors is complicated as the closest following vehicle in the target lane also behaves concerning to the lane change (reaction to the lane-changing intention), i.e., there is apparent interaction between drivers. To model this decision-making properly, this study used the game theoretical approach which is the study of the ways in which interacting choices of players. The game models were developed to enhance the microscopic simulation model representing human driver's realistic lane-changing maneuvers. The stage game structure was designed and payoff functions corresponding to the action strategy sets were formulated using driver's critical decision variables. Furthermore, the repeated game concept which takes previous game results into account was introduced with the assumption that drivers want to maintain initial decision in competition if there is no significant change of situations. The validation results using empirical data provided that the developed stage game has a prediction accuracy of approximately 86%, and the superior performance of the repeated game was verified by an agent-based simulation model, especially in a competitive scenario. Specifically, it helps a simulation model to not fluctuate in decision-making. Based on the validated non-cooperative game model, in addition, this study proposed the cooperative maneuver planning avoiding the non-cooperative maneuvers with prediction of the other vehicle's desired action. If a competitive action is anticipated, in other words, a CAV changes its action to be cooperative without selfish driving. Simulation results showed that the proposed cooperative maneuver planning can improve traffic flow at a freeway merge section. Lastly, the optimal lane selection (OLS) algorithm was also proposed to assist lane selection in consideration of real-time downstream traffic data transferred via a long-range wireless communication. Simulation case study on I-66 highway proved that the proposed OLS can improve the system-wide freeway traffic flow and lane allocation. Overall, the present work addressed developing the game model for merging maneuvers in a traditional transportation system and suggesting use of efficient algorithms in a CAV environment. These findings will contribute to enhance performance of the microscopic simulator and prepare the new era of future transportation system. / Doctor of Philosophy / Driving behaviors considerably affect the traffic flow; especially a lane change occasionally forces rear vehicles in a target lane to decrease speed or stop, hence it is considered as one of primary sources causing traffic congestion. U.S. Department of Transportation (DOT) announced that freeway bottleneck including merge section contributes to freeway traffic congestion more than 40 percent while traffic incidents count for only 25 percent of freeway congestion. This study, therefore, selected a freeway merge section, where mandatory lane changes are required, as a target area for the study. The emerging technologies, such as autonomous vehicles (AVs) and vehicle connectivity, are expected to bring about improvement in mobility, safety, and environment. Based upon these backgrounds, the objective of this study was determined to enhance freeway merge section operations based on the advanced technologies. To achieve the objective, first this study focused on understanding driving behaviors of human drivers. Decision-making for lane-changing behaviors is complicated as the closest following vehicle in the target lane also behaves concerning to the lane change (reaction to the lane-changing intention), i.e., there is apparent interaction between drivers. For example, the vehicle sometimes interferes the merging vehicle's lane-changing by decreasing a gap. To model the decision-making properly, this study modeled the non-cooperative merging behaviors using a game theoretical approach which mathematically explains the interaction (e.g., cooperation or conflict) between intelligent decision-makers. It was modeled for two vehicles, i.e., the merging vehicle in acceleration lane and a following vehicle in freeway rightmost lane, with possible actions of each vehicle. This model includes how each vehicle chooses an action in consideration of rewards. The developed model showed prediction accuracy of approximately 86% against empirical data collected at a merge section on US 101 highway. This study additionally evaluated the proposed model's rational decision-making performance in various merging situations using an agent-based simulation model. These evaluation results indicate that the developed model can depict merging maneuvers based on practical decision-making. Since most existing lane-changing models were developed from the standpoint of the lane-changing vehicle only, this study anticipates that a lane-changing model including practical decision-making process can be used to precisely analyze traffic flow in microscopic traffic simulation. Additionally, an AV should behave as a human-driven vehicle in order to coexist in traditional transportation system, and can predict surrounding vehicle's movement. The developed model in this study can be a part of AV's driving strategy based on perception of human behaviors. In a future transportation environment, vehicle connectivity enables to identify the surrounding vehicles and transfer the data between vehicles. Also, autonomous driving behaviors can be programmed to reduce competition by predicting behaviors of surrounding human-driven vehicles. This study proposed the cooperative maneuver planning which future connected and automated vehicles (CAVs) avoid choosing the non-cooperative actions based on the game model. If a competitive action is anticipated, in other words, a CAV changes its action to be cooperative without selfish driving. Simulation results showed that the proposed cooperative maneuver planning can improve traffic flow at a freeway merge section. Lastly, the optimal lane selection (OLS) algorithm was also proposed to provide a driver the more efficient lane information in consideration of real-time downstream traffic data transferred via a long-range wireless communication. Simulation case study on I-66 highway proved that the proposed OLS can improve the system-wide freeway traffic flow and lane allocation. Overall, the present work addressed developing the game model for merging maneuvers in a traditional transportation system and suggesting use of efficient algorithms in a CAV environment. These findings will contribute to enhance performance of the microscopic simulator and prepare the new era of future transportation system.

Traffic signals are used at intersections to manage the flow of vehicles by allocating right-of-way in a timely manner for different users of the intersection. Traffic signals are therefore installed at an intersection to improve overall safety and to decrease vehicular average delay. However, the variation of driving speed in response to these signals causes an increase in fuel consumption and air emission levels. One solution to this problem is Eco-Cooperative Adaptive Cruise Control (Eco-CACC), which attempts to reduce vehicle fuel consumption and emission levels by optimizing driver behavior in the vicinity of a signalized intersection. Various Eco-CACC algorithms have been proposed by researchers to address this issue. With the help of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication, algorithms are being developed that utilize signal phasing and timing (SPaT) data together with queue information to optimize vehicle trajectories in the vicinity of signalized intersections. The research presented in this thesis constitutes the third phase of a project that entailed developing and evaluating an Eco-CACC system. Its main objective is to evaluate the benefits of the newly developed Eco-CACC algorithm that was proposed by the Center for Sustainable Mobility at the Virginia Tech Transportation Institute. This algorithm uses advanced signal information (SPaT) to compute the fuel-optimal trajectory of vehicles, and, then, send recommended speeds to drivers as an audio message or implement them directly into the subject vehicle. The objective of this study is to quantitatively quantify the fuel-efficiency of the Eco-CACC system in a real field environment. In addition, another goal of this study is to address the implementation issues and challenges with the field application of the Eco-CACC system. A dataset of 2112 trips were collected as part of this research effort using a 2014 Cadillac SRX equipped with a vehicle onboard unit for (V2V) and (V2I) communication. A total of 32 participants between the ages of 18 and 30 were randomly selected from one age group (18-30) with an equal number of males and females. The controlled experiment was conducted on the Virginia Smart Road facility during daylight hours for dry pavement conditions. The controlled field experiment included four different scenarios: normal driving, driving with red indication countdown information provided to drivers, driving with recommended speed information computed by the Eco-CACC system and delivered to drivers, and finally automated driving (automated Eco-CACC system). The controlled field experiment was conducted for four values of red indication offsets along an uphill and downhill approach. The collected data were compared with regard to fuel economy and travel time over a fixed distance upstream and downstream of the intersection (820 ft (250 m) upstream of the intersection to 590 ft (180 m) downstream for a total length of 1410 ft (430 m)). The results demonstrate that the Eco-CACC system is very efficient in reducing fuel consumption levels especially when driving downhill. The field data indicates that the automated scenario could produce fuel and travel time savings of 31% and 9% on average, respectively. In addition, the study demonstrates that driving with a red indication countdown and recommended speed information can produce fuel savings ranging from 4 to 21 percent with decreases in travel times ranging between 1 and 10 percent depending on the value of red indication offset and the direction. Split-split-plot design was used to analyze the data and test significant differences between the four scenarios with regards to fuel consumption and travel time. The analysis shows that the differences between normal driving and driving with either the manual or automated Eco-CACC systems are statistically significant for all the red indication offset values. / Master of Science

In recent years, significant progress has been made in autonomous robotics and the electrification of transportation, highlighting the growing importance of automation in daily life. Ensuring the safety and sustainability of automated systems necessitates the integration of intelligent algorithms capable of making astute decisions in uncertain circumstances. Autonomous robots possess considerable potential for efficiently performing intricate tasks, but this potential can only be unlocked through intelligent algorithms. Moreover, enhancing the energy efficiency of transportation systems yields extensive benefits for the environment, economy, and society at large. Addressing the urgent challenges of climate change and resource depletion necessitates prioritizing energy efficiency in transportation to construct a more resilient and equitable future. This research delves into the development of bio-inspired neural dynamics, nature-inspired swarm intelligence, fuzzy logic, heuristic algorithms, and optimization techniques for optimal control and navigation of electrified and unmanned ground vehicles. Drawing inspiration from biological systems, this research aims to enhance the performance of robots in dynamic and unstructured environments. The approach encompasses a hybrid bio-inspired method, leveraging the mathematical model of a biological neural system's membrane to facilitate smooth trajectory tracking and bounded velocities for a differential drive robot. Additionally, integration of a Leader-Slime Mold Algorithm (L-SMA) for global path optimization and a modified velocity obstacle (MVO) for local motion planning is pursued. A heuristic algorithm is also devised to enhance decision-making in uncertain and dynamic environments by coordinating actions among the L-SMA path planner, the MVO local motion planner, and the enhanced bio-inspired tracking controller. Furthermore, a real-time optimal predictive controller is proposed to address the energy management challenges of electrified vehicles while improving driveability and comfort. This predictive controller employs a linear parameter-varying model of an electrified vehicle, a custom-designed adaptive cost function, and fuzzy logic to adapt a subset of cost function weights. The integration of fuzzy logic and the adaptive predictive controller yields a convex optimization problem solved in real-time using an active-set solver. To further enhance the energy efficiency of the electrified vehicle, a particle swarm optimization enhanced model predictive controller is suggested as an adaptive cruise controller with superior energy efficiency and safety in vehicle-following scenarios. Through these integrated approaches, the aim is to advance the capabilities of autonomous robotics and electrified transportation systems, thereby contributing to safer, more efficient, and sustainable mobility solutions.

Automated vehicle technology can not only transform vehicle behavior on roadways, but also transform users from an active driver to a passenger, with increase in automation levels, such as going from SAE Levels 0 through 2, to Levels 3 through 5. As passengers engage in non-driving related tasks (NDRTs) inside a moving vehicle, they experience limited vehicle control and external awareness. Such conditions can lead to passengers becoming motion sick. Since two out of three passengers are prone to motion sickness, even mild symptoms of motion sickness can severely influence users’ experience in automated vehicles. This dissertation includes four studies to investigate the human factors challenge of motion sickness in passengers of automated vehicles. The first study consists of a systematic literature review following the PRISMA framework. Forty-one papers were selected to be qualitatively analyzed based on which an overarching research framework was proposed. The second study focused on verifying if driving styles simulated on a motion-based driving simulator could be used to artificially induce motion sickness in a safe controlled manner. The third study investigated two driving styles with and without an NDRT to corroborate the findings from the previous study. In the fourth and final study, the focus shifted to mitigating motion sickness. A novel auditory display was developed based on existing literature to reduce motion sickness. Findings from the second and third studies confirmed that strong lateral accelerations could indeed induce motion sickness, and engagement in a cognitively demanding task could lower motion sickness. Based on these findings, the Cognitive Distraction Effect was proposed in the third study. The fourth study, that utilized the verified motion sickness inducing condition from the second and third studies, found that the presence of repeated spatialized anticipatory auditory cues increased motion sickness due to the added sense of vection from the auditory stimuli. This was a unique observation that aligned with recent literature. Furthermore, the fourth study also found evidence in support of the Cognitive Distraction Effect. In summary, this dissertation provides a comprehensive investigation into developing our understanding of motion sickness in passengers of automated vehicles. Three unique contributions are proposed. One, it is possible to induce motion sickness in a safe replicable manner in a laboratory without the need for real-world driving. Second, cognitive engagement in a demanding task can suppress physiological symptoms of motion sickness, suggesting NDRT engagement could have benefits for mitigating motion sickness. Finally, the dissertation sheds new light on the senses that contribute towards development of motion sickness, in that even the hearing system has a role to play in maintaining balance and orientation, in addition to the visual and vestibular systems. / Doctor of Philosophy / The rise of automated vehicle technologies such as Advanced Drives Assistance Systems (ADAS), has the potential to transform drivers into passengers, with increased automation levels requiring less and less user input. These features can benefit users allowing them to utilize their transportation time in a manner of their choosing, while also improving safety. However, when engaging in such tasks that do not allow vehicle occupants to maintain control of their vehicle, or disconnect them from the external environment, passengers can become motion sick, influencing their overall wellbeing. Such conditions can cause users to not want to utilize the advance vehicle automation technologies. To improve users' comfort and experience, the current dissertation undertook research on the topic of motion sickness in passengers of automated vehicles. To that end, four studies were conducted. The first study reviewed the existing literature to identify 41 scientific papers. These papers were analyzed to reveal an overarching research framework that could guide future researchers and students. The second study developed aggressive and soft driving scenarios on a motion-based driving simulator to artificially induce motion sickness in a safe controlled manner. It was verified that the aggressive driving scenario producing sufficiently large lateral accelerations could induce motion sickness. It was also tested if performing a phone task would increase motion sickness, but this did not turn out to be the case. The third study built upon the second study, and tested the effect of being engaged and not engaged on a phone task in both the aggressive and soft driving scenarios. The results showed the effectiveness of the aggressive driving scenario to induce motion sickness, and that engagement in the phone task actually had a mitigating effect on motion sickness. This was explained by the mental distraction presented by the task that led to lower motion sickness. In the last study, the same aggressive driving scenario was used to induce motion sickness, but participants also received spatial auditory alerts before turning; it was expected to lower motion sickness by informing participants about the turn. However, the alerts showed an increase in motion sickness because participants felt increased sense of motion from the auditory alerts before the turns, which aligned with previous findings as well. In addition, the effect of mental distraction on lowering motion sickness was also observed here, confirming findings from the previous studies. Overall, the studies in this dissertation found a way to safely induce motion sickness without the dangers of real-world driving, it identified how being occupied in a task inside the vehicle may have a positive effect on motion sickness, and that auditory alerts should be developed within reason to inform passengers about upcoming motion.

As the desire for deploying automated (“driverless”) vehicles increases, there is a need to understand how they might communicate with other road users in a mixed traffic, urban, setting. In the absence of an active and responsible human controller in the driving seat, who might currently communicate with other road users in uncertain/conflicting situations, in the future, understanding a driverless car’s behaviour and intentions will need to be relayed via easily comprehensible, intuitive and universally intelligible means, perhaps presented externally via new vehicle interfaces. This paper reports on the results of a questionnaire-based study, delivered to 664 participants, recruited during live demonstrations of an Automated Road Transport Systems (ARTS; SAE Level 4), in three European cities. The questionnaire sought the views of pedestrians and cyclists, focussing on whether respondents felt safe interacting with ARTS in shared space, and also what externally presented travel behaviour information from the ARTS was important to them. Results showed that most pedestrians felt safer when the ARTS were travelling in designated lanes, rather than in shared space, and the majority believed they had priority over the ARTS, in the absence of such infrastructure. Regardless of lane demarcations, all respondents highlighted the importance of receiving some communication information about the behaviour of the ARTS, with acknowledgement of their detection by the vehicle being the most important message. There were no clear patterns across the respondents, regarding preference of modality for these external messages, with cultural and infrastructural differences thought to govern responses. Generally, however, conventional signals (lights and beeps) were preferred to text-based messages and spoken words. The results suggest that until these driverless vehicles are able to provide universally comprehensible externally presented information or messages during interaction with other road users, they are likely to contribute to confusing and conflicting interactions between these actors, especially in a shared space setting, which may, therefore, reduce efficient traffic flow.