Dissertations / Theses on the topic 'Connected automated vehicles (CAV)'

The manufacturing of connected and automated vehicles (CAVs) is happening and they are aiming at providing an efficient, safe, and seamless driving experience. This is done by offering automated driving together with wireless communication to and from various objects in the surrounding environment. How automated the vehicle is can be classified from level 0 (no automation at all) to level 5 (fully automated). There is many potential attack vectors of CAVs for attackers to take advantage of and these attack vectors may change depending on what level of automation the vehicle have. There are some known vulnerabilities of CAVs where the security has been breached, but what is seemed to be lacking in the academia in the field of CAVs is a place where the majority of information regarding known attack vectors and cyber-attacks on those is collected. In addition to this the attack vectors may be analyzed for each level of automation the vehicles may have. This research is a systematic literature review (SLR) with three stages (planning, conducting, and report) based on literature review methodology presented by Kitchenham (2004). These stages aim at planning the review, finding articles, extracting information from the found articles, and finally analyzing the result of them. The literature review resulted in information regarding identified cyberattacks and attack vectors the attackers may use as a path to exploit vulnerabilities of a CAV. In total 24 types of attack vectors were identified. Some attack vectors like vehicle communication types, vehicle applications, CAN bus protocol, and broadcasted messages were highlighted the most by the authors. When the attack vectors were analyzed together with the standard of ‘Levels of Driving Automation’ it became clear that there are more vulnerabilities to consider the higher level of automation the vehicle have. The contributions of this research are hence (1) a broad summary of attack vectors of CAVs and (2) a summary of these attack vectors for every level of driving automation. This had not been done before and was found to be lacking in the academia.

Les véhicules autonomes dotés d'un niveau d'automatisation 5 conduiront de manière autonome dans tous les scénarios routiers tels que les autoroutes, les routes enneigées, les zones urbaines ou les embouteillages. L'intégration de la communication V2X, en tant que nouvelle source de perception du véhicule, pourrait supprimer les limitations de la perception locale en communiquant avec un piéton caché par un obstacle ou en détectant à l'avance la présence d'un véhicule caché par un brouillard épais. Cependant, cette communication V2X peut constituer une nouvelle source d'attaques menaçant la perception du véhicule. Les contre-mesures actuelles ne sont pas conçues pour toutes les architectures de véhicules autonomes, car elles requièrent l'assistance du conducteur ou fonctionnent avec un ensemble spécifique de capteurs. La thèse vise donc à proposer une architecture de perception générique et résiliante aux défaillances pour tous les types de véhicules connectés et autonomes. Dans cette thèse, nous proposons une architecture de perception générique nommée GPA avec son algorithme de perception résiliante aux défaillances (FRPA). Nous proposons une nouvelle méthode d'analyse de menaces et d'évaluation des risques nommée SARA, qui identifie et évalue le risque d'attaques ciblant les véhicules connectés et automatisés de niveau 5. Pour identifier où et comment ces attaques ont lieu, nous proposons un modèle d'attaquant et un modèle d'objectifs de sécurité pour tous les systèmes de perception automobile. Nous avons implémenté deux modules de notre algorithme FRPA: un module classification des défaillances basé sur une méthode de Machine Learning et un module de corrélation V2X-Capteur en considérant trois sources d'information: radar, camera et V2X. Nous avons mis en évidence plusieurs nouvelles attaques dans le cycle de perception et soulevé le besoin de nouvelles contre-mesures de sécurité centrées sur l'intégrité physique des infrastructures routières et sur les algorithmes de perception fiables. De plus, nos contre-mesures basées sur l'apprentissage automatique et la corrélation entre capteurs sont très précises pour détecter et classifier les défaillances de perception (score de précision supérieur à 90 %). Enfin, les idées développées dans la thèse ont abouti à 10 brevets déposés et à plusieurs publications
Autonomous vehicles with an automation level 5 will drive autonomously in any road scenarios such as highways, snowy roads, urban areas, or traffic jams. The integration of V2X communication, as a new source of perception for the vehicle could remove the limitations of local perception by communicating with an occluded pedestrian or by detecting in advance the presence of a vehicle under a heavy mist. However, this V2X communication may be a new source of attacks threatening the vehicle perception. Current countermeasures are not designed for all autonomous vehicles because these countermeasures require the driver assistance or work with a specific set of sensors. Therefore, the thesis aims to propose a generic failure resilient perception architecture for all types of connected and autonomous vehicles supporting different kinds of sensors. In this thesis, we propose a generic perception architecture named GPA with its failure resilient perception algorithm (FRPA). We propose a new threat analysis and risk assessment method named SARA that identifies and assess the risk of attacks targeting connected and automated vehicles with an automation level 5. To identify where and how these attacks occur, we propose an attacker and a security goal model for all automotive perception systems. We implemented two modules of our failures resilient perception algorithm (FRPA): a Machine Learning based Failure Classifier and a V2X-Sensor Correlation Module considering three kinds of source: camera, radar, and V2X. We highlighted several new attacks in the perception pipeline and raise the need for new security countermeasures such as the physical integrity of road infrastructures and trustworthy perception algorithms. Besides, our countermeasures based on machine learning and sensor correlation showed very accurate results to detect and classifies perception failures (over 90% accuracy score). Finally, the ideas developed in the thesis resulted in 10 filled patents and several publications

The deployment of Automated Vehicles (AV) is starting to become widespread throughout transportation, resulting in the recognition and awareness by legislative leaders of the potential impact on transportation operations. To assist transportation operators in making the needed preparations for these vehicles, an in-depth study regarding the impact of AV and Connected Automated Vehicles (CAV) is needed. In this research, the impact of AV and CAV on the highway setting is studied. This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, such as the Intelligent Driver Model (IDM), the IDM with traffic adaptive driving Strategy (SIDM), the Improved IDM (IIDM), the IIDM with Constant-Acceleration Heuristic (CAH), and the MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) were examined with the state-of-the-art vehicle trajectory data. The Highway Drone dataset (HighD) were analyzed through the implementation of genetic algorithm to gain more insight about the trajectories of these vehicles. In 2020, there is no commercially available gully automated vehicle available to the public, although many companies are conducting in field testing. This research generated AV trajectories based on the actual vehicle trajectories from the High-D dataset and adjusts those trajectories to account for ideal AV operations. The analysis from the fitted trajectory data shows that the calibrated IIDM with CAH provides a best fit on AV behavior. Next, the AV and CAV were modeled in microscopic perspective to show the impact of these vehicles on a corridor. The traffic simulation software, VISSIM, modified by implementing an external driver model to govern the interactions between Legacy Vehicles (LV), AV, and CAV on a basic and merging highway segment as well as a model of the Interstate 95 corridor south of Richmond, Virginia. From the analysis, this research revealed that the AV and CAV could increase highway capacity significantly. Even with a small portion of AV or CAV, the roadway capacity increased. On I-95, CAV performed better than AV because of Cooperative Adaptive Cruise Control (CACC) and platooning due to CAV's ability to coordinate movement through communication; however, in weaving segments, CAV underperformed AV. This result indicates that the CAV algorithms would need to be flexible in order to maintain flow in areas with weaving sections. Lastly, diverse operational conditions, such as different heavy vehicle market penetration and different aggressiveness were examined to support traffic operators transition to the introduction of AV and CAV. Based on the analysis, the study concludes that the different aggressiveness could mitigate congestion in all cases if the proper aggressiveness level is selected considering the current traffic condition. Overall, the dissertation provides guidance to researchers, traffic operators, and lawmakers to model, simulate, and evaluate AV and CAV on highways.
Doctor of Philosophy
The deployment of Automated Vehicles (AV) is starting to become widespread throughout transportation, resulting in the recognition and awareness by legislative leaders of the potential impact on transportation operations. To assist transportation operators in making the needed preparations for these vehicles, an in-depth study regarding the impact of AV and Connected Automated Vehicles (CAV) is needed. In this research, the impact of AV and CAV on the highway setting is studied. This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, such as the Intelligent Driver Model (IDM), the IDM with traffic adaptive driving Strategy (SIDM), the Improved IDM (IIDM), the IIDM with Constant-Acceleration Heuristic (CAH), and the MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) were examined with the state-of-the-art vehicle trajectory data. The Highway Drone dataset (HighD) were analyzed through the implementation of genetic algorithm to gain more insight about the trajectories of these vehicles. In 2020, there is no commercially available gully automated vehicle available to the public, although many companies are conducting in field testing. This research generated AV trajectories based on the actual vehicle trajectories from the High-D dataset and adjusts those trajectories to account for ideal AV operations. The analysis from the fitted trajectory data shows that the calibrated IIDM with CAH provides a best fit on AV behavior. Next, the AV and CAV were modeled in microscopic perspective to show the impact of these vehicles on a corridor. The traffic simulation software, VISSIM, modified by implementing an external driver model to govern the interactions between Legacy Vehicles (LV), AV, and CAV on a basic and merging highway segment as well as a model of the Interstate 95 corridor south of Richmond, Virginia. From the analysis, this research revealed that the AV and CAV could increase highway capacity significantly. Even with a small portion of AV or CAV, the roadway capacity increased. On I-95, CAV performed better than AV because of Cooperative Adaptive Cruise Control (CACC) and platooning due to CAV's ability to coordinate movement through communication; however, in weaving segments, CAV underperformed AV. This result indicates that the CAV algorithms would need to be flexible in order to maintain flow in areas with weaving sections. Lastly, diverse operational conditions, such as different heavy vehicle market penetration and different aggressiveness were examined to support traffic operators transition to the introduction of AV and CAV. Based on the analysis, the study concludes that the different aggressiveness could mitigate congestion in all cases if the proper aggressiveness level is selected considering the current traffic condition. Overall, the dissertation provides guidance to researchers, traffic operators, and lawmakers to model, simulate, and evaluate AV and CAV on highways.

Connected and Automated Vehicles (CAVs) represent a growing area of study in robotics and automotive research. Their potential benefits of increased traffic flow, reduced on-road accident, and improved fuel economy make them an attractive option. While some autonomous features such as Adaptive Cruise Control and Lane Keep Assist are already integrated into consumer vehicles, they are limited in scope and require innovation to realize fully autonomous vehicles. This work addresses the design problems of perception and planning in CAVs. A decentralized sensor fusion system is designed using Multi-target tracking to identify targets within a vehicle's field of view, enumerate each target with the lane it occupies, and highlight the most important object (MIO) for Adaptive cruise control. Its performance is tested using the Optimal Sub-pattern Assignment (OSPA) metric and correct assignment rate of the MIO. The system has an average accuracy assigning the MIO of 98%. The rest of this work considers the coordination of multiple CAVs from a multi-agent motion planning perspective. A centralized planning algorithm is applied to a space similar to a traffic intersection and is demonstrated empirically to be twice as fast as existing multi-agent planners., making it suitable for real-time planning environments.
Master of Science
Connected and Automated Vehicles are an emerging area of research that involve integrating computational components to enable autonomous driving. This work considers two of the major challenges in this area of research. The first half of this thesis considers how to design a perception system in the vehicle that can correctly track other vehicles and assess their relative importance in the environment. A sensor fusion system is designed which incorporates information from different sensor types to form a list of relevant target objects. The rest of this work considers the high-level problem of coordination between autonomous vehicles. A planning algorithm which plans the paths of multiple autonomous vehicles that is guaranteed to prevent collisions and is empirically faster than existing planning methods is demonstrated.

Congestion and rear-end crashes are two undesirable phenomena of freeway traffic flows, which are interrelated and highly affected by human psychological factors. Since congestion is an everyday problem, and crashes are rare events, congestion management and crash risk prevention strategies are often implemented through separate research directions. However, overwhelming evidence has underscored the inter-relation between rear-end crashes and freeway traffic flow dynamics in recent decades. This dissertation develops novel mathematical models for freeway traffic flow dynamics and safety to integrate them into a unifiable framework. The outcomes of this PhD can enable moving towards faster and safer roads.

Emerging connected and autonomous vehicle technologies (CAV) provide an opportunity to improve highway capacity and reduce adverse impacts of stop-and-go traffic. To realize the potential benefits of CAV technologies, this study provides insightful methodological and managerial tools in microscopic and macroscopic traffic scales. In the macroscopic scale, this dissertation proposes an analytical method to formulate highway capacity for a mixed traffic environment where a portion of vehicles are CAVs and the remaining are human-driven vehicles (HVs). The proposed analytical mixed traffic highway capacity model is based on a Markov chain representation of spatial distribution of heterogeneous and stochastic headways. This model captures not only the full spectrum of CAV market penetration rates but also all possible values of CAV platooning intensities that largely affect the spatial distribution of different headway types. Numerical experiments verify that this analytical model accurately quantifies the corresponding mixed traffic capacity at various settings. This analytical model allows for examination of the impact of different CAV technology scenarios on mixed traffic capacity. We identify sufficient and necessary conditions for the mixed traffic capacity to increase (or decrease) with CAV market penetration rate and platooning intensity. These theoretical results caution scholars not to take CAVs as a sure means of increasing highway capacity for granted but rather to quantitatively analyze the actual headway settings before drawing any qualitative conclusion. In the microscopic scale, this study develops innovative control strategies to smooth highway traffic using CAV technologies. First, it formulates a simplified traffic smoothing model for guiding movements of CAVs on a general one-lane highway segment. The proposed simplified model is able to control the overall smoothness of a platoon of CAVs and approximately optimize traffic performance in terms of fuel efficiency and driving comfort. The elegant theoretical properties for the general objective function and the associated constraints provides an efficient analytical algorithm for solving this problem to the exact optimum. Numerical examples reveal that this exact algorithm has an efficient computational performance and a satisfactory solution quality. This trajectory-based traffic smoothing concept is then extended to develop a joint trajectory and signal optimization problem. This problem simultaneously solves the optimal CAV trajectory function shape and the signal timing plan to minimize travel time delay and fuel consumption. The proposed algorithm simplifies the vehicle trajectory and fuel consumption functions that leads to an efficient optimization model that provides exact solutions. Numerical experiments reveal that this algorithm is applicable to any signalized crossing points including intersections and work-zones. Further, the model is tested with various traffic conditions and roadway geometries. These control approaches are then extended to a mixed traffic environment with HVs, connected vehicles (CVs), and CAVs by proposing a CAV-based speed harmonization algorithm. This algorithm develops an innovative traffic prediction model to estimate the real-time status of downstream traffic using traffic sensor data and information provided by CVs and CAVs. With this prediction, the algorithm controls the upstream CAVs so that they smoothly hedge against the backward deceleration waves and gradually merge into the downstream traffic with a reasonable speed. This model addresses the full spectrum of CV and CAV market penetration rates and various traffic conditions. Numerical experiments are performed to assess the algorithm performance with different traffic conditions and CV and CAV market penetration rates. The results show significant improvements in damping traffic oscillations and reducing fuel consumption.

Over the past few years automotive and technology companies have made significant advances in what has been traditionally a completely human function: driving. Crash avoidance features such as lane departure warning and forward collision warning are becoming increasingly more common and cheaper to obtain, even on non-luxury vehicles. Technology companies and auto manufacturers have announced plans to have self-driving vehicles ready for public use as early as 2020. The mass adoption of automated vehicles (AVs) could significantly change surface transportation as we know it today. This thesis is intended to provide a technical analysis of the potential impacts of AVs on current light-duty vehicle miles traveled (VMT) and parking decisions, the economic desirability of widespread deployment of partially automated technologies, and methods for existing roadways to transition to connected and automated vehicle (CAV) transportation, so that policymakers can make more informed decisions during the transition to CAVs. This work takes a look at AVs from a point in time where vehicles are equipped with driver assistance systems (Level 1) to a point in time where AVs are driverless (Level 5) and can self-park. The results of this work indicate that the fleet-wide adoption of partially automated crash avoidance technologies could provide net-benefit of about $4 billion at current system effectiveness and could provide an annual net-benefit up to $202 billion if all relevant crashes could be prevented. About 25% of all crashes could be addressed by the crash avoidance technologies examined in this dissertation. Over time, as technologies become more effective and cheaper due to economies of scale, greater benefits than the $4 billion could be realized. As automated technologies become more advanced and widespread, existing roadways will need to be able to accommodate these vehicles. This work investigates the effects of a dedicated truck platoon lane on congestion on the Pennsylvania Turnpike and provides a method for existing roadways and highways to determine viable platoon demonstration sites. The initial results suggest that there are several sections of turnpike that could serve as commercial truck platoon demonstration site while still providing a high LOS to all other vehicles. Once AVs can safely and legally drive unoccupied, vehicles will no longer be limited to their driver’s destination and can search for cheaper parking in more distant parking locations. This work simulates a fleet of privately owned vehicles (POVs) in search of cheaper parking in Seattle, using a rectangular grid throughout the study area. Model results indicate that we are not likely to see significant increase in vehicle miles traveled (VMT) and energy use from cars moving from downtown parking lots to cheaper parking in distance locations but at higher penetration rates, parking lot revenues could likely decline to the point where operating a lot is unsustainable economically, if no parking demand management policies are implemented. Driverless vehicles also promise to increase mobility for those in underserved populations. This work estimates bounds on the potential increases in travel in a fully automated vehicle environment due to an increase in mobility from the non-driving and senior populations and people with travel-restrictive medical conditions. Three demand wedges were established in order to conduct a first-order bounding analysis. The combination of the results from all three demand wedges represents an upper bound of 295 billion miles or a 14% increase in annual light-duty VMT for the US population 19 and older. AV technology holds much promise in providing a more accessible and safe transportation system. This thesis can help policymakers and stakeholders maximize the benefits and minimize the challenges.

Assuming that vehicle connectivity technology matures and connected vehicles hit the market, many of the running vehicles will be equipped with highly sophisticated sensors and communication hardware. Along with the goal of eliminating human distracted driving and increasing vehicle automation, it is necessary to develop novel intersection control strategies. Accordingly, the research presented in this dissertation develops an innovative system that controls the movement of vehicles using cooperative cruise control system (CACC) capabilities entitled: iCACC (intersection management using CACC). In the iCACC system, the main assumption is that the intersection controller receives vehicle requests from vehicles and advises each vehicle on the optimum course of action by ensuring no crashes occur while at the same time minimizing the intersection delay. In addition, an innovative framework has been developed (APP framework) using the iCACC platform to prioritize the movements of vehicles based on the number of passengers in the vehicle. Using CACC and vehicle-to-infrastructure connectivity, the system was also applied to a single-lane roundabout. In general terms, this application is considered quite similar to the concept of metering single-lane entrance ramps. The proposed iCACC system was tested and compared to three other intersection control strategies, namely: traffic signal control, an all-way stop control (AWSC), and a roundabout, considering different traffic demand levels ranging from low to high levels of congestion (volume-to-capacity ration from 0.2 to 0.9). The simulated results showed savings in delay and fuel consumption in the order of 90 to 45 %, respectively compared to AWSC and traffic signal control. Delays for the roundabout and the iCACC controller were comparable. The simulation results showed that fuel consumption for the iCACC controller was, on average, 33%, 45% and 11% lower than the fuel consumption for the traffic signal, AWSC and roundabout control strategies, respectively. In summary, the developed iCACC system is an innovative system because of its ability to optimize/model different levels of vehicle automation market penetrations, weather conditions, vehicle classes/models, shared movements, roundabouts, and passenger priority. In addition, the iCACC is capable of capturing the heterogeneity of roadway users (cyclists, pedestrians, etc.) using a video detection technique developed in this dissertation effort. It is anticipated that the research findings will contribute to the application of automated systems, connected vehicle technology, and the future of driverless vehicle management. Finally, the public acceptability of the new advanced in-vehicle technologies is a challenging task and this research will provide valuable feedback for researchers, automobile manufacturers, and decision makers in making the case to introduce such systems.
Ph. D.

This work develops two trajectory planning strategies for future connected automated vehicles (CAVs) to carry out automatic on-ramp merging and mainline facilitating (i.e. gap development) maneuvers. Both proposed strategies are based on constrained optimal control problems. The second strategy features the ability to mitigate the impact of a mainline gap development movement on the traffic following behind so as to enhance traffic safety. Analytical solutions are rigorously derived for the optimal control problems using the Pontryagin Maximum Principle without making any assumptions or ad hoc treatments in order to reduce the analytical difficulty. The obtained analytical solutions are implemented in a model predictive control framework to accommodate constantly changing external environments. Large scale simulations indicate that both the strategies, particularly the second strategy, are beneficial to traffic safety under mixed environments of CAV and human-driven vehicles.

This thesis project investigates the state-of-the-art in traffic management "Green Light Optimal Speed Advisory (GLOSA)" for vehicles in a smart city. GLOSA utilizes infrastructure and vehicles communication through using current signal plan settings and updated vehicular information in order to influence the intersection approach speeds. The project involves traffic microscopic simulations for a mixed traffic environment of conventional and automated vehicles (AVs) both connected to the intersection control and guided by a speed advisory traffic management system. Among the project goals is to assess the effects on traffic performance when human drivers comply to the speed advice. The GLOSA management approach is accessed for its potential to improve traffic efficiency in a full market penetration of connected AVs with absolute compliance. The project also aims to determine the possible outcome resulting from enhancing the AVs capabilities such as implementing short time headways between vehicles in the future.  The best traffic performance results achieved by operating GLOSA goes for connected AVs with the lowest simulated time headway (0.3 sec). The waiting time reduction reaches 95% and trip delay lessens to 88 %.

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.

Cette thèse présente une méthodologie d’ordonnancement cyclique robuste appliquée à la gestion des conteneurs dans les ports maritimes de taille moyenne. Ces derniers sont sujet constamment à des variations des conditions des terminaux, la visibilité réduite sur des évènements futurs ne permet pas de proposer une planification précise des tâches à accomplir. L’ordonnancement cyclique robuste peut jouer un rôle primordial. Il permettra non seulement de proposer un ordonnancement prédictif pour le transport des conteneurs, mais aussi, il proposera également une planification robuste permettant d’éliminer les perturbations éventuelles en temps réel. Dans ce travail nous utilisons les Véhicules Intelligents Automatisés (AIV) pour transporter les conteneurs et nous modélisons les procédures de transit de ces derniers par des graphes d’évènements P-temporels fortement connexes (PTSCEG). Avant l’arrivée d’un porte conteneur au port, un plan (planning) de transport des conteneurs est proposé en un temps court par la programmation linéaire mixte (MIP). Des algorithmes polynomiaux de calcul de robustesse permettent de calculer sur les différents nœuds du système les marges de robustesse. Une fois le navire à quai, l’ordonnancement cyclique robuste est appliqué. Lorsqu’une perturbation est observée (localisée) dans le système, une comparaison avec la marge de robustesse connue est effectuée. Si cette perturbation est incluse dans la marge de robustesse, l’algorithme robuste est utilisé pour éliminer ces perturbations en quelques cycles. Dans le cas où la perturbation est trop importante, la méthode MIP est utilisée pour calculer un nouvel ordonnancement cyclique en un temps réduit
This PhD thesis is dedicated to propose a robust cyclic scheduling methodology applied to container management of medium sized seaport which faces ever changing terminal conditions and the limited predictability of future events and their timing. The robust cyclic scheduling can be seen not just a predictable scheduling to compute a container transportation schedule, but also a reactive scheduling to eliminate the disturbances in real time. In this work, the automated intelligent vehicles (AIV) are used to transport the containers, and the P-time strongly connected event graph (PTSCEG) is used as a graphical tool to model the container transit procedures. Before the arrival of the container vessel, a cyclic container transit schedule can be given by the mixed integer programming (MIP) method in short time. The robustness margins on the nodes of the system can be computed by robustness algorithms in polynomial computing time. After the stevedoring begins, this robust cyclic schedule is used. When a disturbance is observed in system, it should be compared with the known robustness margin. If the disturbance belongs to the robustness margin, the robustness algorithm is used to eliminate the disturbance in a few cycle times. If not, the MIP method is used to compute a new cyclic schedule in short time

Emerging technologies in transportation engineering including connected and automated vehicles (CAVs) exhibit much potential to solve a variety of persistent problems that have impaired the safety and mobility performance of transportation systems. A well-known context of such problems is the construction work zone where agencies have grappled with solutions that range from no closure, partial closure to full closure of road sections during construction, rehabilitation, or maintenance work. Road agencies also seek to develop and implement such workzone plans in a manner that does not unduly jeopardize the economic, social and environmental resources of the road users and the community where the workzone is located. In order to ensure that these three components of sustainable development are attained during road construction workzone management, road agencies seek to develop and implement tools that they can use to guide road users in a network to minimize overall delay, emissions, and fuel consumption. This thesis examines this specific context of highway administration. The thesis developed detour routing guidance for the road users in a road network with work zones in case of full closure, in a manner that is consistent with sustainable development. The research did this for the Automated vehicles (this unlikely scenario is merely considered to demonstrate the potential of connectivity in the network) and the era of connected and automated vehicles. In doing this, the thesis identified the potential benefits that CAV technology can offer in sustainable systemwide management of road work zones. The thesis considered the following sustainability-related evaluation criteria: economic (accessibility to businesses, user costs of fuel consumption, and user costs of travel delay; social (rapid access by emergency services such as ambulance); and environmental (noise pollution and Greenhouse Gas (GHG) emissions). The routing optimization was modeled as a linear programming problem and numerical experiments were carried out. The road network of Sioux Falls city was used to demonstrate the study results. The results suggest that the developed optimal sustainable routing scheme yielded significant improvement in terms of the sustainability criteria while maintaining the acceptable levels of service The results also provided insights on the prospective benefits of routing schemes developed via system optimal management (achieved through centrally-guided detour movements that is facilitated by CAV technology) vis-à-vis user equilibrium management, specifically, Nash Equilibrium.

University of Technology Sydney. Faculty of Engineering and Information Technology.
The extensive progresses in computer science and communication technology in recent decades facilitate the development of the connected and automated vehicles (CAV). Since the emergence of the concept, the commercialization of CAV has been looked forward to providing an effective tool to the regulation of the freeway re-organizing traffic flow who normally initiate the evolvement of the congestion. To analyse the benefits of the CAV on traffic dispersion, the re-organizing traffic in the work zone and the incident-affected zone (under emergency services) were adopted as two cases of non-recurrent congestion, and the microscopic simulations were conducted on the basis of various car-following models and lane-change models. Furthermore, collaborative instances were added to the traditional traffic dynamic models to emulate the motions of the CAV. Trajectories data extracted from NGSIM open-access database were applied to calibrate the Bayes-classifier-based lane-change prediction model in order to better emulate the human drivers’ lane-change decision and to assist the CAV’s collaborations. With the increasing percentage of the CAV, the traffic congestion on the aforementioned bottlenecks were significantly mitigated. While CAV are proved to be capable of facilitate the cooperative lane-changes, they were also trained to refuse the lane-change request if there would be great impact on the target lanes. Although the lane-changes would inevitably impact the target lanes owing to the increasing densities and the disturbances during the lane-change motions, the simulation results showed that CAV are capable of minimizing the negative effects for the entire traffic system’s perspective.

abstract: In large modern urban areas, traffic congestion and fatality have become two serious problems. To improve the safety and efficiency of ground mobility, one promising solution is the cooperative control of connected and automated vehicle (CAV) systems, which can avoid human drivers’ incapability and errors. Taking advantage of two-dimensional (2D) vehicular control, this dissertation intends to conduct a thorough investigation of the modeling, control, and optimization of CAV systems with flocking control. Flocking is a dynamic swarm congregating behavior of a group of agents with self-organizing features, and flocking control of CAV systems attempts to achieve the maintenance of a small and nearly constant distance among vehicles, speed match, destination cohesion, and collision and obstacle avoidance. Concerning artificial multi-agent systems, such as mobile robots and CAV systems, a set of engineering performance requirements should be considered in flocking theory for practical applications. In this dissertation, three novel flocking control protocols are studied, which consider convergence speed, permanent obstacle avoidance, and energy efficiency. Furthermore, considering nonlinear vehicle dynamics, a novel hierarchical flocking control framework is proposed for CAV systems to integrate high-level flocking coordination planning and low-level vehicle dynamics control together. On one hand, using 2D flocking theory, the decision making and motion planning of engaged vehicles are produced in a distributed manner based on shared information. On the other hand, using the proposed framework, many advanced vehicle dynamics control methods and tools are applicable. For instance, in the low-level vehicle dynamics control, in addition to path trajectory tracking, the maintenance of vehicle later/yaw stability and rollover propensity mitigation are achieved by using additional actuators, such as all-wheel driving and four-wheel steering, to enhance vehicle safety and efficiency with over-actuated features. Co-simulations using MATLAB/Simulink and CarSim are conducted to illustrate the performances of the proposed flocking framework and all controller designs proposed in this dissertation. Moreover, a scaled CAV system is developed, and field experiments are also completed to further demonstrate the feasibility of the proposed flocking framework. Consequently, the proposed flocking framework can successfully complete a 2D vehicular flocking coordination. The novel flocking control protocols are also able to accommodate the practical requirements of artificial multi-agent systems by enhancing convergence speed, saving energy consumption, and avoiding permanent obstacles. In addition, employing the proposed control methods, vehicle stability is guaranteed as expected.
Dissertation/Thesis
Doctoral Dissertation Systems Engineering 2020