<div>Collision avoidance design plays an essential role in autonomous vehicle technology. It's an attractive research area that will need much experimentation in the future. This research area is very important for providing the maximum safety to automated vehicles, which have to be tested several times under different circumstances for safety before use in real life. This thesis proposes a method for designing and presenting a collision avoidance maneuver by using a model predictive controller with a moving obstacle for automated vehicles. It consists of a plant model, an adaptive MPC controller, and a reference trajectory. The proposed strategy applies a dynamic bicycle model as the plant model, adaptive model predictive controller for the lateral control, and a custom reference trajectory for the scenario design. The model was developed using the Model Predictive Control Toolbox and Automated Driving Toolbox in Matlab. Builtin tools available in Matlab/Simulink were used to verify the modeling approach and analyze the performance of the system. The major contribution of this thesis work was implementing a novel dynamic obstacle avoidance control method for automated vehicles. The study used validated parameters obtained from previous research. The novelty of this research was performing the studies using a MPC based controller instead of a sliding mode controller, that was primarily used in other studies. The results obtained from the study are compared with the validated models. The comparisons consisted of the lateral overlap,lateral error, and steering angle simulation results between the models. Additionally,this study also included outcomes for the yaw angle. The comparisons and other outcomes obtained in this study indicated that the developed control model produced reasonably acceptable results and recommendations for future studies.</div>

<div> <div> <p>This dissertation discussed the potential benefits that CAV systems can bring to the general well-being, and how the threat lies within the CAV system can affect its performance and functionality.<br></p> <p>Particularly, this dissertation discovered how CAV technology can benefit homeland security and crime investigations involving child abduction crimes. By proposing the initial design network, this dissertation proposed a solution that enhances the current AMBER Alert system using CAV technology. This dissertation also discussed how CAV technology can help perception in corner-case driving scenarios and reduce the risk of traffic accidents, by proposing a dataset that covers various corner cases including different weather and lighting conditions targeting the work zone. Evaluation is made on the collected data and several impact factors have been figured out. </p> <p>This dissertation also discussed an attack scenario that a ROS-based CAV platform was attacked by DoS attacks. We analized the system response after we attacked the system. Discussion and analysis was made on the functionality and stability of the system. </p> <p>Overall, we determined that CAV technology can greatly benefit in general well-being, and threats within the CAV system can cast potential negative benefits once the CAV system is being attacked. </p> </div> </div>

<p>Automated Vehicle Path Following Control (PFC) is an advanced control system that can regulate the vehicle into a collision-free region in the presence of other objects on the road. Common collision avoidance functions, such as forward collision warning and automatic emergency braking, have recently been developed and equipped on production vehicles. However, it is impossible to develop a perfectly precise vehicle model when the vehicle is driving. The most PFC did not consider uncertainties in the vehicle model, external disturbances, and parameter variations at the same time. To address the issues associated with this important feature and function in autonomous driving, a new vehicle PFC is proposed using a robust model predictive control (MPC) design technique based on matrix inequality and the theoretical approach of the hybrid $\&$ switched system. The proposed methodology requires a combination of continuous and discrete states, e.g. regulating the continuous states of the AV (e.g., velocity and yaw angle) and discrete switching of the control strategy that affects the dynamic behaviors of the AV under different driving speeds. Firstly, considering bounded model uncertainties, norm-bounded external disturbances, the system states and control matrices are modified. In addition, the vehicle time-varying longitudinal speed is considered, and a vehicle lateral dynamic model based on Linear Parameter Varying (LPV) is established by utilizing a polytope with finite vertices. Then the Min-Max robust MPC state feedback control law is obtained at every timestamp by solving a set of matrix inequalities which are derived from Lyapunov stability and the minimization of the worst-case in infinite-horizon quadratic objective function. Compared to adaptive MPC, nonlinear MPC, and cascade LPV control, the proposed robust LPV MPC shows improved tracing accuracy along vehicle lateral dynamics. Finally, the state feedback switched LPV control theory with separate Lyapunov functions under both arbitrary switching and average-dwell-time (ADT) switching conditions are studied and applied to cover the path following control in full speed range. Numerical examples, tracking effectiveness, and convergence analysis are provided to demonstrate and ensure the control effectiveness and strong robustness of the proposed algorithms.</p>

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Automated vehicles are those which are capable of sensing their environments in order to perform at least some aspects of the safety-critical control (like steering, throttling, or braking) without direct human input. As a guide for planners and policymakers, the objective of this thesis is to develop a strong foundation for anticipating the potential impacts resulting from advancements in vehicle automation. To establish the foundation, this thesis uses a robust qualitative methodology, coupling a review of literature on the potential advantages and disadvantages of vehicle automation and lessons from past innovations in transportation, with recent trends of the Millennial Generation, carsharing services, and a series of interviews with thought-leaders in automation, planning, policymaking, transportation, and aviation. Five significant findings emerged from this thesis: (1) the impacts of vehicle automation differ depending on one’s visions of what automation means, how it is implemented, what the automation does, and where it operates; (2) current limitations of vehicle automation to perform all aspects of the dynamic driving task in all driving conditions make it difficult to move from level-4 to level-5 automation; (3) level-5 automation is required to have any effect on carsharing, mobility, and quality of life; (4) assuming effective planning and policymaking techniques, housing preferences, urban growth, and increases in total VMT will likely not be significantly impacted by vehicle automation; (5) human drivers may never be allowed to disengage their attention from a partially-automated vehicle, specifically in applications where drivers are expected to reengage their attention in safety-critical situations. From the perspective of understanding the bigger picture, this thesis developed a proposed future scenario of vehicle automation in the next five to ten years that is used to suggest guiding principles for policymakers, and key recommendations for planners, engineers, and researchers.

Vehicle trajectory data has played a critical role in the recent history of traffic flow and CAV operations-related studies. However, available trajectories have limited coverage, either spatial or temporal. The implementation of CAV technology is expected to produce a large-scale trajectory dataset. However, at the initial implementation level, the trajectory data produced is expected to have gaps in terms of completeness. This research develops a data model for large-scale trajectory data that can be built on CAV-collected trajectories and easily manipulated to produce traffic parameters for CAV control and operation research. A benchmarking process has been applied to test a trajectory reconstruction approach to develop a population database from partial trajectories to fill the expected data gap in CAV feedback. The large-scale trajectory data is then used in CAV operations-related studies focusing on CAV's integration with human drivers and developing performance matrices for CAV-controlled optimized trajectories. This research used large-scale vehicle trajectory data from Wide Area Motion Imagery (WAMI) developed by PVLabs for modeling and analyzing traffic characteristics as a surrogate of CAV-collected trajectories. This timestamped location data capture provides trajectory information at an interval of one second. Trajectories from an approximate area of four-square kilometers in downtown Hamilton, Canada, are used to develop a data model to extract and store traffic characteristics. The video data was collected for two three-hour continuous periods, one in the morning and one in the evening of the same day. Like other moving object detection-based algorithms, this data suffers from false-positive detection, false-negative detection, and other positional inaccuracies caused by faulty image registration. A context-based trajectory filtering algorithm has been developed and validated against ten minutes of vehicle counts from actual WAMI images. The filtered data provides a sample of trajectories over the area, including complete and partial vehicle trajectories, excluding undetected ones. The missing trajectory reconstruction process using a dynamic state estimation process is developed to reconstruct partial and missing trajectories. A data analytics approach predicts the number of missing trajectories between two successive detections in the traffic stream on a roadway lane. A benchmarking test of the performance of the missing trajectory prediction algorithm is conducted using the NGSIM I80 database. A frame-by-frame learning method is developed to join the identified missing trajectories. This data analytics approach preserves the naturalistic property of the trajectory, which was a concern of previous traffic-flow model-based approaches. Joining partial/split trajectories provides a more comprehensive picture of the trajectory population. Due to data structure similarities, including the nature of the split and missing trajectories, the methods developed in this study to recover trajectories can be adopted for future CAV feedback data in a mixed traffic scenario. The applicability of using the large-scale trajectory data model is explored in two performance areas of CAV operations. The first is a scenario-based testing process, which evaluates the "intelligence" of a CAV in handling interactions with Human driven Vehicles (HV) by artificially replacing an HV in the traffic stream with a CAV. Scenario-based testing is conducted for a particular Operational Design Domain (ODD). The ODD is defined as operating conditions under which particular driver assistance or automated control systems are designed to function. Existing literature on scenario-based testing primarily focuses on CAV-HV interaction on highways as large-scale naturalistic trajectory data are available to facilitate such studies. This research explores car-following and lane-changing aspects of arterial CAV testing. The large-scale trajectory data model generates testing scenarios and calibrates the surrogate model for CAV operation. The modification to the trajectory data model to accommodate the scenario-based testing is illustrated. The second consists of using the large-scale trajectory data model to estimate a new trajectory smoothness parameter that can indicate the impact of intersection stop-and-go movement on the smoothness of the entire trajectory. This smoothness parameter can be applied as an optimization variable in future trajectory control-based intersection management. Long-duration trajectories from the large-scale trajectory data are used to estimate the spectral arc length parameter for trajectory smoothness. This research only estimates smoothness parameters for human-driven vehicles to illustrate its applicability for vehicle trajectories. This research developed a framework for applying expected partial trajectories from CAV technology in estimating near-complete trajectories. The large-scale data application process in two CAV operations-related studies is also provided. / Doctor of Philosophy / The decision-making process undertaken by transportation agencies for planning, evaluating, and operating transportation facilities relies on analyzing traffic and driver behavior for prevailing and future traffic conditions. The analytical tools for policy, design, decision-making, and safety analysis use aggregated and disaggregated traffic parameters. Traffic parameters are information about the dynamic state of the traffic. In the case of a vehicle, the dynamic state information can be location, speed, acceleration, heading, and spacing with other vehicles in the traffic stream. The sequence of these dynamic parameters is called vehicle trajectories in a broader term. The trajectory information is collected using several direct and indirect collection systems. The implementation of CAV technologies is expected to provide a new source of vehicle trajectory information. Trajectory data are integral to CAV safety, operational evaluation, and optimization control algorithms. Trajectory data are also used to develop, calibrate, and validate the models representing a particular aspect of human driver behavior, and the recent development of CAV has elevated the necessity and application of trajectory data. As a result, a significant demand exists in academia and industry for the procedure to create trajectories of the vehicle population in the traffic stream. The trajectory population represents the dynamic properties of all the vehicles moving over the data collection area. The primary goal of this research is to develop and apply a large-scale trajectory population database. Trajectories are typically stored in a Moving Object Database (MOD). This research leverages a MOD database collected by a new generation of Wide-Area Motion Imagery (WAMI). The WAMI system collects images from a high-altitude moving aerial platform with high-definition cameras at a fixed time interval, which captures the trajectories of vehicles in the collection area. However, validating the created trajectories for completeness and data noise revealed continuity and consistency gaps in trajectories. A multistep data mining process is undertaken to filter, process, and extract sample trajectories with reduced data noise. A trajectory reconstruction task is undertaken to reduce the data gap. A benchmarking performance test for trajectory reconstruction is conducted using NGSIM I80 data because it has been validated in multiple studies and contains trajectories of all vehicles during the collection period (i.e., trajectory population). The trajectory reconstruction methodology developed in this research can be adapted for future CAV-collected partial trajectory data. The development of the trajectory reconstruction methodology and training data created from NGSIM I80 is one of the main contributions of this research in the field of trajectory reconstruction. Several traffic flow measures are then estimated from the sample trajectories that outline the analytical requirements to integrate trajectory data with roadway infrastructure. A data model is developed to store and manipulate dynamic trajectory parameters efficiently. The resulting data processing and integration process can be applied to CAV-collected trajectories to create an analytical trajectory database. The large-scale trajectory database is used to illustrate its capability in evaluating CAV operating models, specifically the car-following and lane-changing models on an arterial network. The car-following model mimics the longitudinal movement of real-world drivers following another vehicle. The lane-changing model predicts lane-changing behavior due to path-planning requirements and navigating surrounding traffic conditions. The overall operational model evaluation process is called accelerated evaluation, in which the naturalistic vehicle movement data is used to measure CAV's operational and safety performance. For a second application of the large-scale trajectory data, long-duration trajectories are used to develop a trajectory smoothness performance measure that can be used to test different trajectory control approaches for intersection movement management. This research is one of the early attempts to leverage large-scale vehicle trajectory datasets in transportation engineering applications. Its primary contribution is the development of a comprehensive trajectory validation methodology that can be applied to future CAV feedback to produce a trajectory population database with enhanced analytical capability. The secondary output of this research is benchmarking results for different analytical methodologies to develop the trajectories that can be used in future research and development as a reference.

The introduction of automated vehicles (AVs) changes the way road users interact and communicate. In AVs, informal communication such as eye contact or gestures with other road users is omitted. Because interaction should still be objectively and subjectively safe, many studies are currently focusing on the communication processes between (automated) vehicles and predominantly vulnerable road users (VRUs), like pedestrians and cyclists [1 ]. These road users are highly at risk of being fatally injured in road traffic accidents, with the WHO reporting pedestrians and cyclists account for 32 % of all fatalities in Europe [2].... This shows why it is so important to study the interaction processes between VRUs, such as cyclists, and A Vs in real traffic. The algorithms of the A Vs must be able to anticipate the behavior of VRUs and thus ensure a subjectively and objectively safe interaction (cyclists should feel and be safe a.round them). This is the aim of the present study. How do cyclists behave when they encounter an apparent A V for the first time? How do they assess the situation and on what basis do they decide to cross? To answer these questions, a field study will take place in Munich in the summer of 2022, in which such interactions will be observed and the cyclists will be interviewed afterward. The study takes place within the TEMPUS project funded by the BMDV (German Federal Ministry for Digital and Transport). [From: Introduction]

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