The DARPA Urban Challenge in 2007 showed that fully autonomous vehicles, driven by computers without human intervention on public roads, are technologically feasible with current intelligent vehicle technology [6]. Some researchers predict that within 5-20 years there will be autonomous vehicles for sale on the automobile market. Therefore, the time is right to rethink our current transportation infrastructure, which is primarily designed for human drivers, not autonomous vehicles. The Autonomous Intersection Management (AIM) project at UT Austin aims to propose a large-scale, real-time framework to be a substitute for current traffic light and stop signs. Automobiles in modern urban settings spend a lot of time idling at intersections. In 2007, US drivers wasted 4.16 billion hours of their time and 2.81 billion gallons of gas in congestion, costing a total of 87.2 billion dollars nationwide [18]. A big portion of this waste takes place at intersections. The AIM project is able to utilize the capacity of intersections to minimize time waste and fuel consumption. The fundamental idea of Autonomous Intersection management (AIM) [13] is a reservation system in which cells in space-time will be reserved by the au- tonomous vehicles based on their trajectories. An intersection manager takes care of the reservation as well as communication with the vehicles. This mechanism tries to maximize the usage of the intersection area. It ensures a collision free intersection as well. The main question of this project is what intersection control mechanism is appropriate for reducing an autonomous vehicle's waiting time and improving the throughput of the intersection. Previous work proposed the first-come-first-served (FCFS) policy in which the reservation requests are served as soon as they are received. The results of simulation show that FCFS outperforms the current traffic systems, traffic light and stop sign, by orders of magnitude. We, however, observe that FCFS performs suboptimal in certain traffic patterns that are pretty common in urban settings. In this project, first we study the limitations of FCFS, then develop a more efficient policy to alleviate these limitations. The idea that we examined is a systematic policy of granting reservations that have the objective of minimizing the cost of delaying vehicles. In an attempt to build the system in reality, we used miniature robots called Eco-be. Due to their cost and size, Eco-bes are good candidates for testing a multi-agent system with a large number of agents. In spite of the fact that the physical challenges of Eco-bes do not perfectly match those of full size autonomous vehicles, they are still useful for demonstration and education purposes as well as for the study of collisions for which experiments with full size vehicles are costly and dangerous. / text

Safe and efficient traffic control remains a challenging task with the continued increase in the number of vehicles, especially in urban areas. This manuscript focuses on traffic control at intersections, since urban roads with closely spaced intersections are often prone to queue spillbacks, which disrupt traffic flows across the entire network and increase congestion. While various intelligent traffic control solutions exist for autonomous systems, they are not applicable to or ineffective against human-operated vehicles or mixed traffic. On the other hand, existing approaches to manage intersections with human-operated vehicles, cannot adequately adjust to dynamic traffic conditions. This manuscript presents a technology-agnostic adaptive real-time server based approach to dynamically determine signal timings at an intersection based on changing traffic conditions and queue lengths (i.e., wait times) to minimize, if not eliminate, spillbacks without unnecessarily increasing delays associated with intersection crossings. We also provide timeliness guarantee bounds by analyzing the travel time delays, hence making our approach more dependable and predictable. The proposed approach was validated in simulations and on a realistic hardware testbed with robots mimicking human driving behaviors. Compared to the pre-timed traffic control and an adaptive scheduling based traffic control, our algorithm is able to avoid spillbacks under highly dynamic traffic conditions and improve the average crossing delay in most cases by 10--50 %. / Master of Science / Safe and efficient traffic control remains a challenging task with the continued increase in the number of vehicles, especially in urban areas. This manuscript focuses on traffic control at intersections, since urban roads with closely spaced intersections are often prone to congestion that blocks other intersection upstream, which disrupt traffic flows across the entire network. While various intelligent traffic control solutions exist for autonomous systems, they are not applicable to or ineffective against human-operated vehicles or mixed traffic. On the other hand, existing approaches to manage intersections with human-operated vehicles, cannot adequately adjust to dynamic traffic conditions. This work presents a technologyagnostic adaptive approach to dynamically determine signal timings at an intersection based on changing traffic conditions and queue lengths (i.e., wait times) to minimize, if not eliminate, spillbacks without unnecessarily increasing delays associated with intersection crossings. We also provide theoretical bounds to guarantee the performance of our approach in terms of the travel delays that may incur on the vehicles in the system, hence making our approach more dependable and predictable. The proposed approach was validated in simulations and on a realistic hardware testbed which uses robots to mimic human driving behaviour in an urban environment. Comparisons with widely deployed and state-of-the-art traffic control techniques show that our approach is able to minimize spillbacks as well as improve on the average crossing delay in most cases.

Dans ce travail, nous exploitons le potentiel offert par les véhicules autonomes coopératifs, pour fluidifier le trafic dans une intersection isolée puis dans un réseau d’intersections. Nous proposons le protocole SVAC (Système du Véhicule Actionneur Coopératif) permettant de réguler une intersection isolée. SVAC est basé sur une distribution individuelle du droit de passage qui respecte un ordre précis donné par une séquence de passage.Pour optimiser la séquence de passage, nous définissons la politique PED (Politique d’Evacuation Distribuée) permettant d’améliorer le temps d’évacuation total de l’intersection. La création de la séquence de passage est étudiée à travers deux modélisations. Une modélisation sous forme de graphes permettant le calcul de la solution optimale en connaissant les dates d’arrivée de tous les véhicules, et une modélisation de type réseaux de Petri avec dateurs pour traiter la régulation temps-réel. Des tests réels avec des véhicules équipés ont été réalisés pour étudier la faisabilité du protocole SVAC. Des simulations mettant en jeu un trafic réaliste permettent ensuite de montrer la capacité de fluidifier le trafic par rapport à une régulation classique par feux tricolores.La régulation d’un réseau d’intersections soulève les problèmes d’interblocage et de réorientation du trafic. Nous proposons le protocole SVACRI (Système du Véhicule Actionneur Coopératif pour les Réseaux d’Intersections) qui permet d’éliminer à priori les risques d’interblocage à travers la définition de contraintes d’occupation et de réservation de l’espace et du temps. Nous étudions également la possibilité d’améliorer la fluidité du trafic à travers le routage des véhicules, en tirant avantage du protocole SVACRI. Enfin, nous généralisons le système de régulation proposé pour la synchronisation des vitesses aux intersections. / The objective of this work is to use the potential offered by the wireless communication and autonomous vehicles to improve traffic flow in an isolated intersection and in a network of intersections. We define a protocol, called CVAS (Cooperative Vehicle Actuator System) for managing an isolated intersection. CVAS distributes separately the right of way to each vehicle according to a specific order determined by a computed sequence.In order to optimize the sequence, we define a DCP (Distributed Clearing Policy) to improve the total evacuation time of the intersection. The control strategy is investigated through two modeling approaches. First graph theory is used for calculating the optimal solution according to the arrival times of all vehicles, and then a timed Petri Net model is used to propose a real-time control algorithm. Tests with real vehicles are realized to study the feasibility of CVAS. Simulations of realistic traffic flows are performed to assess our algorithm and to compare it versus conventional traffic lights.Managing a network of intersections raises the issue of gridlock. We propose CVAS-NI protocol (Cooperative Vehicle actuator system for Networks of Intersections), which is an extension of CVAS protocol. This protocol prevents the deadlock in the network through occupancy and reservation constraints. With a deadlock free network we extend the study to the traffic routing policy. Finally, we generalize the proposed control system for synchronizing the vehicle velocities at intersections.

Vehicle stops and speed variations account for a large percentage of vehicle fuel losses especially at signalized intersections. Recently, researchers have attempted to develop tools that reduce these losses by capitalizing on traffic signal information received via vehicle connectivity with traffic signal controllers. Existing state-of-the-art approaches, however, only consider surrogate measures (e.g. number of vehicle stops, time spent accelerating and decelerating, and/or acceleration or deceleration levels) in the objective function and fail to explicitly optimize vehicle fuel consumption levels. Furthermore, the majority of these models do not capture vehicle acceleration and deceleration limitations in addition to vehicle-to-vehicle interactions as constraints within the mathematical program. The connectivity between vehicles and infrastructure, as achieved through Connected Vehicles technology, can provide a vehicle with information that was not possible before. For example, information on traffic signal changes, traffic slow-downs and even headway and speed of lead vehicles can be shared. The research proposed in this dissertation uses this information and advanced computational models to develop fuel-efficient vehicle trajectories, which can either be used as guidance for drivers or can be attached to an electronic throttle controlled cruise control system. This fuel-efficient cruise control system is known as an Eco-Cooperative Adaptive Cruise Control (ECACC) system. In addition to the ECACC presented here, the research also expands on some of the key eco-driving concepts such as fuel-optimizing acceleration models, which could be used in conjunction with conventional vehicles and even autonomous vehicles, or assistive systems that are being implemented in vehicles. The dissertation first presents the results from an on-line survey soliciting driver input on public perceptions of in-vehicle assistive devices. The results of the survey indicate that user-acceptance to systems that enhance safety and efficiency is ranked high. Driver–willingness to use advanced in-vehicle technology and cellphone applications is also found to be subjective on what benefits it has to offer and safety and efficiency are found to be in the top list. The dissertation then presents the algorithmic development of an Eco-Cooperative Adaptive Cruise Control system. The modeling of the system constitutes a modified state-of-the-art path-finding algorithm within a dynamic programming framework to find near-optimal and near-real-time solutions to a complex non-linear programming problem that involves minimizing vehicle fuel consumption in the vicinity of signalized intersections. The results demonstrated savings of up to 30 percent in fuel consumption within the traffic signalized intersection vicinity. The proposed system was tested in an agent-based environment developed in MATLAB using the RPA car-following model as well as the Society of Automobile Engineers (SAE) J2735 message set standards for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. The results showed how multi-vehicle interaction enhances usability of the system. Simulation of a calibrated real intersection showed average fuel savings of nearly 30 percent for peak volumes. The fuel reduction was high for low volumes and decreased as the traffic volumes increased. The final testing of the algorithm was done in an enhanced Traffic Experimental Simulation tool (eTEXAS) that incorporates the conventional TEXAS model with a new web-service interface as well as connected vehicle message set dictionary. This testing was able to demonstrate model corrections required to negate the effect of system latencies as well as a demonstration of using SAE message set parsing in a connected vehicle application. Finally, the dissertation develops an integrated framework for the control of autonomous vehicle movements through intersections using a multi-objective optimization algorithm. The algorithm integrated within an existing framework that minimizes vehicle delay while ensuring vehicles do not collide. A lower-level of control is introduced that minimizes vehicle fuel consumption subject to the arrival times assigned by the upper-level controller. Results show that the eco-speed control algorithm was able to reduce the overall fuel-consumption of autonomous vehicles passing through an intersection by 15 percent while maintaining the 80 percent saving in delay when compared to a traditional signalized intersection control. / Ph. D.

Intelligent transport systems are preparing to welcome connected and automated vehicles (CAVs), although it is uncertain which algorithms should be employed for the effective and efficient management of CAV systems. Even though remarkable improvements in telecommunication technologies, such as vehicle-to-everything (V2X), enable communication and computation sharing among different agents, e.g. vehicles and infrastructures, within existing approaches, a significant part of the computation burden is still typically assigned to central units. Distributed algorithms, on the other hand, could alleviate traffic units from most, if not all, of the high dimensional calculation duties, while improving security and remaining effective. In this paper, we propose a formation-control-inspired distributed algorithm to rearrange vehicles’ passing time periods through an intersection and a novel formulation of the underlying trajectory optimization problem so that vehicles need to exchange and process only a limited amount of information. We include early simulation results to demonstrate the effectiveness of our approach.

No description available.

La conduite autonome a attiré une attention croissante au cours des dernières décennies en raison de son potentiel impact socio-économique, notamment concernant la réduction du nombre d'accidents de la route et l'amélioration de l'efficacité du trafic. Grâce à l'effort de plusieurs instituts de recherche et d'entreprises, les véhicules autonomes ont déjà accumulé des dizaines de millions de kilomètres parcourus dans des conditions de circulation réelles. Cette thèse porte sur la conception d'une architecture de contrôle pour les véhicules autonomes et coopératifs dans l'optique de leur déploiement massif. La base commune des différentes architectures proposées dans cette thèse est le Contrôle-Commande Prédictif, reconnu pour son efficacité et sa polyvalence. Nous présentons tout d'abord une architecture classique de contrôle hiérarchique, qui utilise la commande prédictive pour planifier un déplacement (choix de trajectoire), puis déterminer un contrôle permettant de suivre cette trajectoire. Toutefois, comme nous le montrons dans un deuxième temps, cette architecture simple n'est pas capable de gérer certaines contraintes logiques, notamment liées aux règles de circulation et à l'existence de choix de trajectoires discrets. Nous proposons donc approche hybride de la commande prédictive, que nous utilisons pour développer une architecture de contrôle pour un véhicule autonome individuel. Nous étudions le problème de contrôler un ensemble de véhicules autonomes circulant en convoi, i.e. maintenir une formation prédéterminée sans intervention humaine. Pour ce faire, nous utilisons à nouveau une architecture hiérarchique basée sur la commande prédictive, composée d'un superviseur de convoi et de contrôleurs pour chaque véhicule individuel. Enfin, nous proposons encore une architecture pour le problème de coordonner un groupe de véhicules autonomes dans une intersection sans feux de circulation, en utilisant un contrôleur d'intersection et en adaptant les contrôleurs des véhicules individuels pour leur permettre de traverser l'intersection en toute sécurité. / Autonomous driving has been gaining more and more attention in the last decades, thanks to its positive social-economic impacts including the enhancement of traffic efficiency and the reduction of road accidents. A number of research institutes and companies have tested autonomous vehicles in traffic, accumulating tens of millions of kilometers traveled in autonomous driving. With the vision of massive deployment of autonomous vehicles, researchers have also started to envision cooperative strategies among autonomous vehicles. This thesis deals with the control architecture design of individual autonomous vehicles and cooperative autonomous vehicles. Model Predictive Control (MPC), thanks to its efficiency and versatility, is chosen as the building block for various control architectures proposed in this thesis. In more detail, this thesis first presents a classical hierarchical control architecture for individual vehicle control that decomposes the controller into a motion planner and a tracking controller, both using nonlinear MPC. In a second step, we analyze the inability of the proposed planner in handling logical constraints raised from traffic rules and multiple maneuver variants, and propose a hybrid MPC based motion planner that solves this issue. We then consider the convoy control problem of autonomous vehicles in which multiple vehicles maintain a formation during autonomous driving. A hierarchical formation control architecture is proposed composing of a convoy supervisor and local MPC based vehicle controllers. Finally, we consider the problem of coordinating a group of autonomous vehicles at an intersection without traffic lights. A hierarchical architecture composed of an intersection controller and multiple local vehicle controllers is proposed to allow vehicles to cross the intersection smoothly and safely.

Le déploiement des futurs véhicules autonomes promet d'avoir un impact socio-économique majeur, en raison de leur promesse d'être à la fois plus sûrs et plus efficaces que ceux conduits par des humains. Afin de satisfaire à ces attentes, la capacité des véhicules autonomes à planifier des trajectoires sûres et à manœuvrer efficacement dans le trafic sera capitale. Cependant, le problème de planification de trajectoire au milieu d'obstacles statiques ou mobiles a une combinatoire forte qui est encore aujourd'hui problématique pour les meilleurs algorithmes.Cette thèse explore une nouvelle approche de la planification de mouvement, basée sur l'utilisation de la notion de décision de conduite comme guide pour structurer le problème de planification en vue de faciliter sa résolution. Cette approche peut trouver des applications pour la conduite coopérative, par exemple pour coordonner plusieurs véhicules dans une intersection non signalisée, ainsi que pour la conduite autonome où chaque véhicule planifie sa trajectoire. Dans le cas de la conduite coopérative, les décisions correspondent au choix d'un ordonnancement des véhicules qui peut être avantageusement encodé comme un graphe. Cette thèse propose une représentation similaire pour la conduite autonome, où les décisions telles que dépasser ou non un véhicule sont nettement plus complexes. Une fois la décision prise, il devient aisé de déterminer la meilleure trajectoire y correspondant, en conduite coopérative comme autonome. Cette approche basée sur la prise de décision peut permettre d'améliorer la robustesse et l'efficacité de la planification de trajectoire, et ouvre d'intéressantes perspectives en permettant de combiner des approches mathématiques classiques avec des techniques plus modernes d'apprentissage automatisé. / The deployment of future self-driving vehicles is expected to have a major socioeconomic impact due to their promise to be both safer and more traffic-efficient than human-driven vehicles. In order to live up to these expectations, the ability of autonomous vehicles to plan safe trajectories and maneuver efficiently around obstacles will be paramount. However, motion planning among static or moving objects such as other vehicles is known to be a highly combinatorial problem, that remains challenging even for state-of-the-art algorithms. Indeed, the presence of obstacles creates exponentially many discrete maneuver choices, which are difficult even to characterize in the context of autonomous driving. This thesis explores a new approach to motion planning, based on using this notion of driving decisions as a guide to give structure to the planning problem, ultimately allowing easier resolution. This decision-based motion planning approach can find applications in cooperative driving, for instance to coordinate multiple vehicles through an unsignalized intersection, as well as in autonomous driving where a single vehicle plans its own trajectory. In the case of cooperative driving, decisions are known to correspond to the choice of a relative ordering for conflicting vehicles, which can be conveniently encoded as a graph. This thesis introduces a similar graph representation in the case of autonomous driving, where possible decisions -- such as overtaking the vehicle at a specific time -- are much more complex. Once a decision is made, planning the best possible trajectory corresponding to this decision is a much simpler problem, both in cooperative and autonomous driving. This decision-aware approach may lead to more robust and efficient motion planning, and opens exciting perspectives for combining classical mathematic programming algorithms with more modern machine learning techniques.

Mobility is a topic of great interest in research and engineering since critical aspects such as safety, traffic efficiency, and environmental sustainability still represent wide open challenges for researchers and engineers. In this thesis, at first, we address the cooperative driving safety problem both from a centralized and decentralized perspective. Then we address the problem of optimal energy management of hybrid vehicles to improve environmental sustainability, and finally, we develop an intersection management systems for Connected Autonomous Vehicle to maximize the traffic efficiency at an intersection. To address the first two topics, we define a common framework. Both the cooperative safety and the energy management for Hybrid Electric Vehicle requires to model the driver behavior. In the first case, we are interested in evaluating the safety of the driver’s intentions, while in the second case, we are interested in predicting the future velocity profile to optimize energy management in a fixed time horizon. The framework is the Co-Driver, which is, in short, a bio-inspired agent able both to model and to imitate a human driver. It is based on a layered control structure based on the generation of atomic human-like longitudinal maneuvers that compete with each other like affordances. To address driving safety, the Co-Driver behaves like a safe driver, and its behavior is compared to the actual driver to understand if he/she is acting safely and providing warnings if not. In the energy management problem, the Co-Driver aims at imitating the driver to predict the future velocity. The Co-Driver generates a set of possible maneuvers and selects one of them, imitating the action selection process of the driver. At first, we address the problem of safety by developing and investigating a framework for Advanced Driving Assistance Systems (ADAS) built on the Co-Driver. We developed and investigated this framework in an innovative context of new intelligent road infrastructure, where vehicles and roads communicate. The infrastructure that allows the roads to interact with vehicles and the environment is the topic of a research project called SAFESTRIP. This project is about deploying innovative sensors and communication devices on the road that communicate with all vehicles. Including vehicles that are equipped with Vehicle-To-Everything (V2X) technology and vehicles that are not, using an interface (HMI) on smart-phones. Co-Driver-based ADAS systems exploit connections between vehicles and (smart) roads provided by SAFESTRIP to cover several safety-critical use cases: pedestrian protection, wrong-way vehicles on-ramps, work-zones on roads and intersections. The ADAS provide personalized warning messages that account for the adaptive driver behavior to maximize the acceptance of the system. The ability of the framework to predict human drivers’ intention is exploited in a second application to improve environmental sustainability. We employ it to feed with the estimated speed profile a novel online Model Predictive Control (MPC) approach for Hybrid Electric Vehicles, introducing a state-of-the-art electrochemical model of the battery. Such control aims at preserving battery life and fuel consumption through equivalent costs. We validated the approach with actual driving data used to simulate vehicles and the power-train dynamics. At last, we address the traffic efficiency problem in the context of autonomous vehicles crossing an intersection. We propose an intersection management system for Connected Autonomous Vehicles based on a bi-level optimization framework. The motion planning of the vehicle is provided by a simplified optimal control problem, while we formulate the intersection management problem (in terms of order and timing) as a Mixed Integer Non-Linear Programming. The latter approximates a linear problem with a powerful piecewise linearization technique. Therefore, thanks to this technique, we can bound the error and employ commercial solvers to solve the problem (fast enough). Finally, this framework is validated in simulation and compared with the "Fist-Arrived First-Served" approach to show the impact of the proposed algorithm.

Cette thèse est consacrée à étudier les applications potentielles de véhicules autonomes et communications V2X pour construire les systèmes de transport intelligents. Premièrement, le comportement de caravane dans un environnement de véhicule connecté est étudié. Un algorithme de commande de caravane est conçu pour obtenir l'espacement sécuritaire ainsi que la conformité de la vitesse et de l'accélération. Deuxièmement, à plus grande échelle, les caravanes autour d'une intersection sont considérées. Le débit pendant une période de signal de trafic peut être amélioré en tirant profit de la capacité redondante de la route. Dans diverses contraintes, les véhicules peuvent choisir d'accélérer et rejoindre la caravane précédente ou à décélérer de déroger à l'actuel. Troisièmement, une intersection sans signalisation en VANET est considérée. Dans des conditions de faible trafic, les véhicules peuvent réguler leur vitesse avant d'arriver à l'intersection en fonction du temps d'occupation de la zone de conflit (TOZC) stocké au niveau du gestionnaire, afin qu'ils puissent traverser l'intersection sans collision ni arrêt. Le délai peut être réduit en conséquence. Enfin, un algorithme de gestion d'intersection autonome universelle, qui peut fonctionner même avec le trafic lourd, est développé. Le véhicule cherche à sécuriser les fenêtres entrant dans le TOZC. Ensuite, sur la base des fenêtres trouvées et le mouvement du véhicule qui précède, les trajectoires des véhicules peuvent être planifiées en utilisant une méthode de programmation dynamique segmentée. Tous les algorithmes conçus sont testés et vérifiés avec succès par des simulations dans scénarios différents / This thesis is devoted to study the potential applications of autonomous vehicles and V2X communications to construct the intelligent transportation systems. Firstly, the behavior of platoon in connected vehicle environment is studied. A platoon control algorithm is designed to obtain safe spacing as well as accordance of velocity and acceleration for vehicles in the same lane. Secondly, in larger scale, the platoons around an intersection are considered. The throughput in a traffic signal period can be improved by taking advantage of the redundant road capacity. Within diverse constraints, vehicles can choose to accelerate to join in the preceding platoon or to decelerate to depart from the current one. Thirdly, an unsignalized intersection in VANET is considered. In light traffic conditions, vehicles can regulate their velocities before arriving at the intersection according to the conflict zone occupancy time (CZOT) stored at the manager, so that they could get through the intersection without collision or stop. The delay can be reduced accordingly. Finally, an universal autonomous intersection management algorithm, which can work even with heavy traffic, is developed. The vehicle searches for safe entering windows in the CZOT. Then based on the found windows and the motion of preceding vehicle, the trajectories of vehicles can be planned using a segmented dynamic programming method. All the designed algorithms are successfully tested and verified by simulations in various scenarios