With limited road infrastructure and increasing number of vehicles on the road, an intelligent transport system is needed to increase the throughput in traffic and minimize traffic jams in highly populated areas. The purpose of this project is to design and implement a control system that is capable of driving and following the preceding vehicle autonomously in the longitude direction only. The vehicle is also equipped with a vehicle to vehicle communication unit. With this information, all vehicles on the road can communicate with each other and are able to achieve shorter distances between vehicles and damp any disturbance caused by upstream traffic. A general structure on Cooperative Adaptive Cruise Control (CACC) is created by studying the research from The Netherlands Organization for Applied Scientific Research (TNO). A string stability criterion is used to determine if the system is suitable of driving in a platoon, where a string of vehicles are following a lead vehicle. This system is then implemented in a Volvo S60 and has participated in the 2011 Grand Cooperative Driving Challenge hosted in The Netherlands. The results show that the system has ability to increase throughput and damp disturbance on the upstream traffic by communicating with the other vehicles ahead. The system is also robust and simple enough to earn the 2nd place in the competition. / Grand Cooperative Driving Challenge

Traffic congestion and safety has become a major issue in the modern world's commute. Congestion has been causing people to travel billions of hours more and to purchase billions of gallons of fuel extra which account to congestion cost of billions of dollars. Autonomous driving vehicles have been one solution to this problem because of their huge impact on efficiency, pollution, and human safety. Also, extensive research has been carried out on control design of vehicular platoons because a further improvement in traffic throughput while not compromising the safety is possible when the vehicles in the platoon are provided with better predictive abilities. Motion control is a key area of autonomous driving research that handles moving parts of vehicles in a deliberate and controlled manner. A widely worked on problem in motion control concerned with time parameterized reference tracking is trajectory tracking. Having an efficient and effective tracking algorithm embedded in the autonomous driving system is the key for better performance in terms of resources consumed and tracking error. Many tracking control algorithms in literature rely on an accurate model of the vehicle and often, it can be an intimidating task to come up with an accurate model taking into consideration various conditions like friction, heat effects, ageing processes etc. And typically, control algorithms rely on periodic execution of the tasks that update the control actions, but such updates might not be required, which result in unnecessary actions that waste resources. The main focus of this work is to design an intermittent model-free optimal control algorithm in order to enable autonomous vehicles to track trajectories at high-speeds. To obtain a solution which is model-free, a Q-learning setup with an actor-network to approximate the optimal intermittent controller and a critic network to approximate the optimal cost, resulting in the appropriate tuning laws is considered. / Master of Science / A risen research effort in the area of autonomous vehicles has been witnessed in the past few decades because these systems improve safety, comfort, transport time and energy consumption which are some of the main issues humans are facing in the modern world’s highway systems. Systems like emergency braking, automatic parking, blind angle vehicle detection are creating a safer driving environment in populated areas. Advanced driver assistance systems (ADAS) are what such kind of systems are known as. An extension of these partially automated ADAS are vehicles with fully automated driving abilities, which are able to drive by themselves without any human involvement. An extensively proposed approach for making traffic throughput more efficient on existing highways is to assemble autonomous vehicles into platoons. Small intervehicle spacing and many vehicles constituting each platoon formation improve the traffic throughput significantly. Lately, the advancements in computational capabilities, in terms of both algorithms and hardware, communications, and navigation and sensing devices contributed a lot to the development of autonomous systems (both single and multiagent) that operate with high reliability in uncertain/dynamic operating conditions and environments. Motion control is an important area in the autonomous vehicles research. Trajectory-tracking is a widely studied motion control scenario which is about designing control laws that force a system to follow some time-dependent reference path and it is important to have an effective and efficient trajectory-tracking control law in an autonomous vehicle to reduce the resources consumed and tracking error. The goal of this work is to design an intermittent model-free trajectory tracking control algorithm where there is no need of any mathematical model of the vehicle system being controlled and which can reduce the controller updates by allowing the system to evolve in an open loop fashion and close the loop only when an user defined triggering condition is satisfied. The approach is energy efficient in that the control updates are limited to instances when they are needed rather than unnecessary periodic updates. Q-learning which is a model-free reinforcement learning technique is used in the trajectory tracking motion control algorithm to make the vehicles track their respective reference trajectories without any requirement of their motion model, the knowledge of which is generally needed when dealing with a motion control problem. The testing of the designed algorithm in simulations and experiments is presented in this work. The study and development of a vehicle platform in order to perform the experiments is also discussed. Different motion control and sensing techniques are presented and used. The vehicle platform is shown to track a reference trajectory autonomously without any human intervention, both in simulations and experiments, proving the effectiveness of the proposed algorithm.

No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / An Adaptive Cruise Control (ACC) system is a driver assistance system that assists a driver to improve driving safety and driving comfort. The design of ACC controller often involves the design of a switching logic that decides where and when to switch between the two modes in order to ameliorate driving comfort, mitigate the chance of a potential collision with the preceding vehicle while reduce long-distance driving load from the driver. In this thesis, a new strategy for designing ACC controller is proposed. The proposed control strategy utilizes Range vs. Range-rate chart to illustrate the relationship between headway distance and velocity difference, and then find out a constant deceleration trajectory on the chart, which the following vehicle is controlled to follow. This control strategy has a shorter elapsed time than existing ones while still maintaining a relatively safe distance during transient process. String stability issue has been addressed by many researchers after the adaptive cruise control (ACC) concept was developed. The main problem is when many vehicles with ACC controller forming a vehicle platoon end to end, how the control algorithm is designed to ensure that the spacing error, which is the deviation of the actual range from the desired headway distance, would not amplify as the number of following vehicles increases downstream along the platoon. In this thesis, string stability issues have been taken into consideration and constraints of parameters of an ACC controller are derived to mitigate steady state error propagation.

L'adoption récente et généralisée des systèmes d'automatisation des véhicules, avec l’incorporation de la connectivité entre voitures, a encouragé l’utilisation des techniques comme le Contrôle Croisière Adaptatif Coopératif (CACC) et la conduite en convoi. Ces techniques ont prouvé l’amélioration du flux de trafic et la sécurité de la conduite, tout en réduisant la consommation d’énergie et les émissions CO_2. Néanmoins, la robustesse et la stabilité stricte du convoi, malgré les délais de communication et l’hétérogénéité des convois, restent des sujets de recherche en cours. Cette thèse a pour sujet la conception, l’analyse et validation de systèmes de contrôle pour le car-following automatisé et coopératif, en ciblant l’augmentation de ses avantages et son usage, en se concentrant sur la robustesse et la stabilité du convoi même sur des séries de véhicules hétérogènes avec des retards de communication. Une structure feedforward/feedback est développée, dont sa modularité est fondamentale pour la mise au point des approches avec des objectifs différents mais complémentaires. L’architecture permet non seulement l’adoption d’une stratégie d’espacement pour la range entière de vitesse, mais elle peut aussi être employée dans le cadre d’un CACC basé sur une machine d’état pour la conduite en convoi sur des environnements urbains avec des capacités de freinage d’urgence et de rejoint du convoi. Des différents algorithmes pour la conception de systèmes de contrôle feedback pour la régulation des distances sont présentés, pour quoi le calcul d’ordre fractionnaire démontre fournir des réponses fréquentielles de boucle fermé plus précises et satisfaire des besoins plus exigeantes. La performance est assurée malgré l’hétérogénéité avec la proposition de deux approches feedforward différents. Le premier est basé sur une topologie en considérant que le véhicule précédent dans la boucle, tandis que le deuxième inclut le véhicule leader pour améliorer la performance de suivi. Les algorithmes proposés sont validés avec des études de stabilité dans le domaine du temps et fréquence, ainsi que simulations et expérimentations réelles. / Recent widespread adoption of vehicle automation and introduction of vehicle-to-vehicle connectivity has opened the doors for techniques as Cooperative Adaptive Cruise Control (CACC) and platooning, showing promising results in terms of traffic capacity and safety improvement, while reducing fuel consumption and CO_2 emissions. However, robustness and strict string stability, despite communication delays and string heterogeneity is still an on-going research field. This thesis deals with the design, study and validation of control systems for cooperative automated car-following, with the purpose of extending their benefits and encourage their employment, focusing on robustness and string stability, despite possible V2V communication delays and string heterogeneity. A feedforward/feedback hierarchical control structure is developed, which modularity is fundamental for the proposal of approaches that target different but complementary performance objectives. The architecture not only permits the adoption of a full speed range spacing policy that target multiple criteria, but can also be employed in a state machine-based CACC framework for urban environments with emergency braking and platoon re-joining capabilities in case of pedestrian interaction. Different feedback control design algorithms are presented for the gap-regulation, for which the fractional-order calculus is demonstrated to provide more accurate closed loop frequency responses and satisfy more demanding requirements. Desired performance is ensured in spite of string heterogeneity through the proposal of two feedforward methods : one based on predecessor-only topology, while the second includes the leader vehicle information on feedforward to gain tracking capabilities. Proposed control algorithms are validated through time and frequency-domain stability studies, simulation and real platforms experiments.