The expectations of potential users of Connected and Automated Vehicles (CAVs) reveal howusers conceptualize the technology, how they expect it to serve them and what they need from the service. Previous studies of CAVs have concentrated research on user adoption, willingness to use, pay, and future challenges of the technology. However, a few studies have explored the expectations of potentialusers of CAVs. The knowledge of the expectations of potential users is essential for service designers to understand the needs of each category of users to enhance user-level satisfaction and prioritize different alternatives for service improvements. Through a qualitative and explorative study of potential users inSweden, this study presents three categories of the expectations of potential users of CAVs: Optimistic,Pessimistic, and Contradictory expectations. The Optimistic expectations represent potential users'positive insights of what they need for CAVs to be a successful innovation. The Pessimistic expectations relate to the potential user's hope that adverse events will happen in the introduction of CAVs and thatthe service will produce negative outcomes. The Contradictory expectations are conflicting expectations that potential users have for CAVs which share both optimistic and pessimistic views. This studydiscusses the implications of the categories of the expectations of potential users for service designersand researchers. The study also proposes future recommendations for the extension of this researchwork.

One of the biggest challenges in the development ofautonomous vehicles is to anticipate the behavior of other roadusers. Autonomous vehicles rely on data obtained by on-boardsensors and make decisions accordingly, but this becomes difficultif the sensors are occluded or have limited range. In this reportwe propose an algorithm for connected vehicles in an intersectionto fuse and share sensor data and gain a better estimationof the surrounding environment. The method used for sensorfusion was a Kalman filter and a tracking algorithm, where timedelay from external sensors was considered. Parameters for theKalman filter were decided through measurement of the sensors’variances as well as tuning. It was concluded that the variancesare dependent on the objects’ movements, which means thatconstant parameters for the Kalman filter would not be enoughto make it efficient. However, the tracking and the sensor sharingmade a significant difference in the vehicle’s detection rate whichcould ultimately increase safety in intersections. / En av de största utmaningarna för utvecklingen av autonoma fordon är att förutse andra trafikanters beteenden. Autonoma fordon förlitar sig på data från sensorer ombord och fattar beslut i enlighet med informationen från dessa. Detta blir särskilt svårt om sensorerna skyms eller om sensorerna har begränsad räckvidd. I denna rapport föreslår vi en algoritm för delning och optimering av sensordata för autonoma fordon i en vägkorsning för att ge fordonet en så bra uppfattning av omgivningen som möjligt. Metoden som användes för sensorfusion var ett Kalman-filter tilsammans med en spårningsalgoritm där tidsfördröjning av data från externa sensorer togs i beaktning. Parametrarna för Kalman-filtret valdes genom mätning av sensorns varians samt genom trimning. Slutsatsen drogs att varianserna är beroende av objektens rörelsemönster, vilket innebär att konstanta parametrar för Kalman-filtret inte skulle vara tillräckligt för att göra det funktionellt. Spårningen och delningen av sensordata gjorde emellertid en betydande skillnad i andelen upptäckta objekt vilket skulle kunna nyttjas för att öka säkerheten i korsningar. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

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Optimizing connected automated vehicle movements through roadway intersections is a challenging problem. Traditional traffic control strategies, such as traffic signals are not optimal, especially for heavy traffic. Alternatively, centralized automated vehicle control strategies are costly and not scalable given that the ability of a central controller to track and schedule the movement of hundreds of vehicles in real-time is highly questionable. In this research, a series of fully distributed heuristic algorithms are proposed where vehicles in the vicinity of an intersection continuously cooperate with each other to develop a schedule that allows them to safely proceed through the intersection while incurring minimum delays. An algorithm is proposed for the case of an isolated intersection then a number of algorithms are proposed for a network of intersections where neighboring intersections communicate directly or indirectly to help the distributed control at each intersection makes a better estimation of traffic in the whole network. An algorithm based on the Godunov scheme outperformed optimized signalized control. The simulated experiments show significant reductions in the average delay. The base algorithm is successfully added to the INTEGRATION micro-simulation model and the results demonstrate improvements in delay, fuel consumption, and emissions when compared to roundabout, signalized, and stop sign controlled intersections. The study also shows the capability of the proposed technique to favor emergency vehicles, producing significant increases in mobility with minimum delays to the other vehicles in the network. / Ph. D.

Objectives: Research suggests the general public has a lack of faith in highly automated vehicles (HAV) stems from a lack of system transparency while in motion (e.g., the user not being informed on roadway perception or anticipated responses of the car in certain situations). This problem is particularly prevalent in public transit or ridesharing applications, where HAVs are expected to debut, and when the user has minimal training on, and control over, the vehicle. To improve user trust and their perception of comfort and safety, this study aimed to develop more detailed and tailored human-machine interfaces (HMI) aimed at relying automated vehicle intended actions (i.e., "intentions") and perceptions of the driving environment to the user. Methods: This project developed HMI systems, with a focus on visual and auditory displays, and implemented them into a HAV developed at the Virginia Tech Transportation Institute (VTTI). Volunteer participants were invited to the Smart Roads at VTTI to experience these systems in real-world driving scenarios, especially ones typically found in rideshare or public transit operations. Participant responses and opinions about the HMIs and their perceived levels of comfort, safety, trust, and situational awareness were captured via paper-based surveys administered during experimentation. Results: There was a considerable link found between HMI modality and users' reported levels of comfort, safety, trust, and situational awareness during experimentation. In addition, there were several key behavioral factors that made users more or less likely to feel comfortable in the HAV. Conclusions: Moving forward, it will be necessary for HAVs to provide ample feedback to users in an effort to increase system transparency and understanding. Feedback should consistently and accurately represent the driving landscape and clearly communicate vehicle states to users. / Master of Science / One of the greatest barriers to the entry of highly automated vehicles (HAV) into the market is the lack of user trust in the vehicle. Research has shown that this lack of faith in the system primarily stems from a lack of system transparency while in motion (e.g., the user not being told how the car will react in a certain situation) and not having an effective way to control the vehicle in the event of a system failure. This problem is particularly prevalent in public transit or ridesharing applications, where HAVs are expected to first appear and where the user has less training and control over the vehicle. To improve user trust and perceptions of comfort and safety, this study developed human-machine interface (HMI) systems, focusing on visual and auditory displays, to better relay automated vehicle "intentions" and the perceived driving environment to the user. These HMI systems were then implemented into a HAV developed at the Virginia Tech Transportation Institute (VTTI) and tested with volunteer participants on the Smart Roads.

The commercial deployment of automated vehicles (AVs) is around the corner. With the development of automation technology, automobile and IT companies have started to test automated vehicles. Waymo, an automated driving technology development company, has recently opened the self-driving service to the public. The advancement in this emerging mobility option also drives transportation reasearchers and urban planners to conduct automated vehicle-related research, especially to gain insights on the impact of automated vehicles (AVs) in order to inform policymaking. However, the variation with urban form, the heterogeneity of mode choice, and the impacts at disaggregated levels lead to the dynamics of the impacts of AVs, which not comprehensively understood yet. Therefore, this dissertation extends existing knowledge base by understanding the dynamics of the impacts from three perspectives: (1) examining the role of urban form in the performance of SAV systems; (2) exploring the heterogeneity of AV mode choices across regions; and (3) investigating the distribution of energy consumption in the era of AVs. To examine the first aspect, Shared AV (SAV) systems are simulated for 286 cities and the simulation outcomes are regressed on urban form variables that measure density, diversity, and design. It is suggested that the compact development, a multi-core city pattern, high level of diversity, as well as more pedestrian-oriented networks can promote the performance of SAVs measured using service efficiency, trip pooling success rate, and extra VMT generation. The AV mode choice behaviors of private conventional vehicle (PCV) users in Seattle and Knasas City metropolitan areas are examined using an interpretable machine learning framework based on an AV mode choice survey. It is suggested that attitudes and trip and mode-specific attributes are the most predictive. Positive attitudes can promote the adoption of PAVs. Longer PAV in-vehicle time encourages the residents to keep the PCVs. Longer walking distance promotes the usage of SAVs. In addition, the effects of in-vehicle time and walking distance vary across the two examined regions due to distinct urban form, transportation infrustructure and cultural backgrounds. Kansas City residents can tolerate shorter walking distance before switching to SAV choices due to the car-oriented environment while Seattle residents are more sensitive to in-vehicle travel time because of the local congestion levels. The final part of the dissertation examines the demand for energy of AVs at disaggregated levels incorporating heterogeneity of AV mode choices. A three-step framework is employed including the prediction of mode choice, the determination of vehicle trajectories, and the estimation of the demand for energy. It is suggested that the AV scenario can generate -0.36% to 2.91% extra emissions and consume 2.9% more energy if gasoline is used. The revealed distribution of traffic volume suggests that the demand for charging is concentrated around the downtown areas and on highways if AVs consume electricity. In summary, the dissertation demonstrates that there is a dynamics with regard to the impacts and performance of AVs across regions due to various urban form, infrastructure and cultural environment, and the spatial heterogeneity within cities. / Doctor of Philosophy / Automated vehicles (AVs) have been a hot topic in recent years especially after various IT and automobile companies announced their plans for making AVs. Waymo, an automated driving technology development company, has recently opened the self-driving service to the public. Automated vehicles, which are defined as being able to self-drive, self-park, and automate routing, provide potentials for new business models such as privately owned automated vehicles (PAVs) that serve trips within households, shared AVs (SAVs) that offer door-to-door service to the public who request service using app-based platforms, and SAVs with pool where multiple passengers may be pooled together when the vehicles do not detour much if sequentially picking up and dropping off passengers. Therefore, AVs can transform the transportation system especially by reducing vehicle ownership and increasing travel distance. To plan for a sustainable future, it is important to gain an understanding of the impacts of AVs under various scenarios. Thus, a wealth of case studies explore the system performance of SAVs such as served trips per SAV per day. However, the impacts of AVs are not static and tend to vary across cities, depend on heterogeneous mode choices within regions, and may not be evenly distributed within a city. Therefore, this dissertation fills the research gaps by (1) investigating how urban features such as density may influence the system performance of SAVs; (2) exploring heterogeneity of key factors that influence the decisions about using AVs across regions; and (3) examining the distribution of the demand for energy in the era of AVs. The first study in the dissertation simulates the SAVs that serve trips within 286 cities and examines the relationship between the system performance of SAVs and city features such as density, diversity, and design. The system performance of SAVs is evaluated using served trips per SAV per day, percent of pooled trips that allow ridesharing, and percent of extra Vehicle Miles Traveled (VMT) compared to the VMT requested by the served trips. The results suggest that compact diverse development patterns and pedestrian-oriented networks can promote the performance of SAVs. The second study uses an interpretable machine learning framework to understand the heterogeneous mode choice behaviors of private car users in the era of AVs in two regions. The framework uses an AV mode choice survey, where respondents are asked to take mode choice experiments given attributes about the trips, to train machine learning models. Accumulated Local Effects (ALE) plots are used to analyze the model results. ALE outputs the accumulated change of the probability of choosing specific modes within small intervals across the range of the variable of interest. It is suggested that attitudes and trip-specific attributes such as in-vehicle time are the most important determinants. Positive attitudes, longer trips, and longer walking distance can promote the adoption of AV modes. In addition, the effects of in-vehicle time and walking distance vary across the two examined regions due to distinct urban form, transportation infrastructure, and cultural backgrounds. Kansas City residents can tolerate shorter walking distance before switching to SAV choices due to the car-oriented environment while Seattle residents are more sensitive to in-vehicle travel time because of the local congestion levels. The final part of the dissertation examines the demand for energy of AVs at disaggregated levels incorporating heterogeneity of AV mode choices. A three-step framework is employed including the prediction of mode choice, the determination of vehicle trajectories, and the estimation of the demand for energy. It is suggested that the AV scenario can generate -0.36% to 2.91% of extra emissions and consume 2.9% more energy compared to a business as usual (BAU) scenario if gasoline is used. The revealed distribution of traffic volume suggests that the demand for charging is concentrated around the downtown areas and on highways if AVs consume electricity. In summary, the dissertation demonstrates that there is a dynamics with regard to the impacts and performance of AVs across regions due to various urban form, infrastructure and cultural environment, and the spatial heterogeneity within cities.

Automated vehicles (AVs) are promising applications of artificial intelligence (AI). While human drivers benefit from AVs, including long-distance support and collision prevention, we do not always understand how AV systems function and make decisions. Consequently, drivers might develop inaccurate mental models and form unrealistic expectations of these systems, leading to unwanted incidents. Although efforts have been made to support drivers' understanding of AVs through in-vehicle visual and auditory interfaces and warnings, these may not be sufficient or effective in addressing user confusion and overtrust in in-vehicle technologies, sometimes even creating negative experiences. To address this challenge, this dissertation conducts a series of studies to explore the possibility of using the in-vehicle intelligent agent (IVIA) in the form of the speech user interface to support drivers, aiming to enhance safety, performance, and satisfaction in conditionally automated vehicles. First, two expert workshops were conducted to identify design considerations for general IVIAs in the driving context. Next, to better understand the effectiveness of different IVIA designs in conditionally automated driving, a driving simulator study (n=24) was conducted to evaluate four types of IVIA designs varying by embodiment conditions and speech styles. The findings indicated that conversational agents were preferred and yielded better driving performance, while robot agents caused greater visual distraction. Then, contextual inquiries with 10 drivers owning vehicles with advanced driver assistance systems (ADAS) were conducted to identify user needs and the learning process when interacting with in-vehicle technologies, focusing on interface feedback and warnings. Subsequently, through expert interviews with seven experts from AI, social science, and human-computer interaction domains, design considerations were synthesized for improving the explainability of AVs and preventing associated risks. With information gathered from the first four studies, three types of adaptive IVIAs were developed based on human-automation function allocation and investigated in terms of their effectiveness on drivers' response time, driving performance, and subjective evaluations through a driving simulator study (n=39). The findings indicated that although drivers preferred more information provided to them, their response time to road hazards might be degraded when receiving more information, indicating the importance of the balance between safety and satisfaction. Taken together, this dissertation indicates the potential of adopting IVIAs to enhance the explainability of future AVs. It also provides key design guidelines for developing IVIAs and constructing explanations critical for safer and more satisfying AVs. / Doctor of Philosophy / Automated vehicles (AVs) are an exciting application of artificial intelligence (AI). While these vehicles offer benefits like helping with long-distance driving and preventing accidents, people often do not understand how they work or make decisions. This lack of understanding can lead to unrealistic expectations and potentially dangerous situations. Even though there are visual and sound alerts in these cars to help drivers, they are not always sufficient to prevent confusion and over-reliance on technology, sometimes making the driving experience worse. To address this challenge, this dissertation explores the use of in-vehicle intelligent agents (IVIAs), in the form of speech assistant, to help drivers better understand and interact with AVs, aiming to improve safety, performance, and overall satisfaction in semi-automated vehicles. First, two expert workshops helped identify key design features for IVIAs. Then, a driving simulator study with 24 participants tested four different designs of IVIAs varying in appearance and how they spoke. The results showed that people preferred conversational agents, which led to better driving behaviors, while robot-like agents caused more visual distractions. Then, through contextual inquiries with 10 drivers who own vehicles with advanced driver assistance systems (ADAS), I identified user needs and how they learn to interact with in-car technologies, focusing on feedback and warnings. Subsequently, I conducted expert interviews with seven professionals from AI, social science, and human-computer interaction fields, which provided further insights into facilitating the explainability of AVs and preventing associated risks. With the information gathered, three types of adaptive IVIAs were developed based on whether the driver was actively in control of the vehicle, or the driving automation system was in control. The effectiveness of these agents was evaluated through drivers' brake and steer response time, driving performance, and user satisfaction through another driving simulator study with 39 participants. The findings indicate that although drivers appreciated more detailed explanations, their response time to road hazards slowed down, highlighting the need to balance safety and satisfaction. Overall, this research shows the potential of using IVIAs to make AVs easier to understand and safer to use. It also offers important design guidelines for creating these IVIAs and their speech contents to improve the driving experience.

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An optimal-control based path planning algorithm has been developed recently for Connected and Automated Vehicles (CAVs) driving on a lane-free highway, including vehicle nudging. That vehicle movement strategy considers, in the lateral direction, a lateral desired speed that had been set to zero in previous works; in other words, vehicles avoid lateral movement if this is not helpful in achieving some of their goals, e.g. achieving a longitudinal desired speed by overtaking slower vehicles. In this work, a lateral positioning strategy for the vehicles is proposed, aiming to improve the vehicles’ longitudinal speeds and the traffic flow, mainly at intermediate densities, by distributing laterally the vehicles based on their longitudinal desired speeds. The intention is to leverage the existing optimal control formulation to move the CAVs to appropriate lateral positions, while respecting other, higher-priority sub-objectives, such as avoiding crashes. First, the longitudinal desired speed of each vehicle is mapped to a lateral desired position under the premise “faster vehicles drive farther left”. Then, the value of the desired lateral speed is updated in real-time in dependence on the vehicle’s current versus the desired lateral position, letting the optimal control problem, with the given sub-objective priorities, decide on the actual vehicle path. The proposed strategy is demonstrated via traffic simulations, involving various traffic densities, on a ring-road. Several quantities, such as the reached average flows and statistical measures of the error in the lateral position are computed for evaluation and comparison purposes.

Cycling offers many benefits, such as reducing traffic congestion, Iower emissions and health benefits. To further promote cycling, the cyclists' perceived safety needs to be addressed. In this context, automated vehicles offer high potential for designing safe and comfortable interactions with cyclists in the future. A key parameter in these interactions constitutes the proximity of vehicles passing cyclists to avoid causing discomfort. To evaluate specific scenarios with varying proximity, cycling simulators provide a safe and standardized environment for traffic safety research. Therefore, there are numerous efforts to implement cycling simulators for use in research. However, it is important to verify the simulator validity to ensure the generalizability of results. In this work, an implementation of a virtual reality (VR) cycling simulation is presented and it is aimed to investigate the simulator validity in terms of perceived criticality in traffic conflict scenarios as well as the participants' experience of presence within the VR cycling simulation. [from Introduction]