The operation of groups of heavy-duty vehicles at small inter-vehicular distances, known as platoons, lowers the overall aerodynamic drag and, therefore, reduces fuel consumption and greenhouse gas emissions. Experimental tests conducted on a flat road and without traffic have shown that platooning has the potential to reduce the fuel consumption up to 10%. However, platoons are expected to drive on public highways with varying topography and traffic. Due to the large mass and limited engine power of heavy-duty vehicles, road slopes can have a significant impact on feasible and optimal speed profiles. Therefore, maintaining a short inter-vehicular distance without coordination can result in inefficient or even infeasible speed trajectories. Furthermore, external traffic can interfere by affecting fuel-efficiency and threatening the safety of the platooning vehicles. This thesis addresses the problem of safe and fuel-efficient control for heavy-duty vehicle platooning. We propose a hierarchical control architecture that splits this complex control problem into two layers. The layers are responsible for the fuel-optimal control based on look-ahead information on road topography and the real-time vehicle control, respectively. The top layer, denoted the platoon coordinator, relies on a dynamic programming framework that computes the fuel-optimal speed profile for the entire platoon. The bottom layer, denoted the vehicle control layer, uses a distributed model predictive controller to track the optimal speed profile and the desired inter-vehicular spacing policy. Within this layer, constraints on the vehicles' states guarantee the safety of the platoon. The effectiveness of the proposed controller is analyzed by means of simulations of several realistic scenarios. They suggest a possible fuel saving of up to 12% for the follower vehicles compared to the use of existing platoon controllers. Analysis of the simulation results shows how the majority of the fuel saving comes from a reduced usage of vehicles brakes. A second problem addressed in the thesis is model predictive control for obstacle avoidance and lane keeping for a passenger car. We propose a control framework that allows to control the nonlinear vehicle dynamics with linear model predictive control. The controller decouples the longitudinal and lateral vehicle dynamics into two successive stages. First, plausible braking and throttle profiles are generated. Second, for each profile, linear time-varying models of the lateral dynamics are derived and used to formulate a collection of linear model predictive control problems. Their solution provides the optimal control input for the steering and braking actuators. The performance of the proposed controller has been evaluated by means of simulations and real experiments. / <p>QC 20150911</p>

This thesis proposes a planning framework for Autonomous Electric Vehicle (AEV) charging. The framework is intended to help transportation decision-makers determine Electric Vehicle (EV) charging facility locations and capacities for the mixed fleet of Autonomous Vehicle (AV) and Human-driven Vehicle (HDV). The bi-level nature of the framework captures the decision-making processes of the transportation agency decision-makers and travelers, thereby providing solid theoretical and practical foundations for the EV charging network design. At the upper level, the decision-makers seek to determine the locations and operating capacities of the EV charging facilities, in a manner that minimizes total travel time and construction costs subject to budgetary limitations. In addition, the transportation decision-makers provide AV-exclusive lanes to encourage AV users to reduce travel time, particularly at wireless-charging lanes, as well as other reasons, including safety. At the lower level, the travelers seek to minimize their travel time by selecting their preferred vehicle type (AV vs. HDV) and route. In measuring the users delay costs, the thesis considered network user equilibrium because the framework is designed for urban networks where travelers route choice affects their travel time. The bi-level model is solved using the Non-Dominated Sorting Genetic Algorithm (NSGA-II) algorithm.

No description available.

La présence de systèmes et d'objets embarqués communicants dans notre vie quotidienne nous a apporté une myriade d'avantages, allant de l'ajout de commodité et de divertissement à l'amélioration de la sûreté de nos déplacements et des soins de santé. Cependant, les défauts et les vulnérabilités de ces systèmes exposent leurs utilisateurs à des risques de dommages matériels, de pertes financières, et même des dommages corporels. Par exemple, certains véhicules commercialisés, qu'ils soient connectés ou conventionnels, ont déjà souffert d'une variété de défauts de conception entraînant des blessures et la mort. Dans le même temps, alors que les véhicules sont de plus en plus connectés (et dans un avenir proche, autonomes), les chercheurs ont démontré la possibilité de piratage de leurs capteurs ou de leurs systèmes de contrôle interne, y compris l'injection directe de messages sur le bus CAN.Pour assurer la sûreté des utilisateurs et des passants, il faut considérer plusieurs facteurs. La sûreté conventionnelle suggère qu'un système ne devrait pas contenir de défauts logiciels et matériels qui peuvent l'empêcher de fonctionner correctement. La "sûreté de la fonction attendue" consiste à éviter les situations que le système ou ses composants ne peuvent pas gérer, comme des conditions environnementales extrêmes. Le timing peut être critique pour certains systèmes en temps réel, car afin d'éviter des situations dangereuses, le système devra réagir à certains événements, comme l'évitement d'obstacles, dans un délai déterminé. Enfin, la sûreté d'un système dépend de sa sécurité. Un attaquant qui peut envoyer des commandes fausses ou modifier le logiciel du système peut changer son comportement et le mettre dans diverses situations dangereuses. Diverses contre-mesures de sécurité et de sûreté pour les systèmes embarqués, en particulier les véhicules connectés, ont été proposées. Pour mettre en oeuvre correctement ces contre-mesures, il faut analyser et vérifier que le système répond à toutes les exigences de sûreté, de sécurité et de performance, et les faire la plus tôt possible dans les premières phases de conception afin de réduire le temps de mise sur le marché, et éviter les reprises. Cette thèse s'intéresse à la sécurité et la sûreté des les systèmes embarqués, dans le contexte du véhicule autonome de l'Institut Vedecom. Parmi les approches proposées pour assurer la sûreté et la sécurité des les systèmes embarqués, l'ingénierie dirigée par modèle est l'une de ces approches qui couvre l'ensemble du processus de conception, depuis la définition des exigences, la conception du matériel et des logiciels, la simulation/vérification formelle et la génération du code final. Cette thèse propose une méthodologie de modélisation pour une conception sûre et sécurisée, basée sur la méthodologie SysML-Sec, qui implique de nouvelles méthodes de modélisation et de vérification. La modélisation de la sécurité est généralement effectuée dans les dernières phases de la conception. Cependant, la sécurité a un impact sur l'architecture/allocation; les décisions de partitionnement logiciel/matériel devraient être prises en fonction de la capacité de l'architecture à satisfaire aux exigences de sécurité. Cette thèse propose comment modéliser les mécanismes de sécurité et l'impact d'un attaquant dans la phase de partitionnement logiciel/matériel. Comme les protocoles de sécurité ont un impact négatif sur le performance d'un système, c'est important de mesurer l'utilisation des composants matériels et les temps de réponse du système. Des composants surchargés peuvent entraîner des performances imprévisibles et des retards indésirables. Cette thèse traite aussi des mesures de latence des événements critiques pour la sécurité, en se concentrant sur un exemple critique pour les véhicules autonomes : le freinage/réponse après la détection d'obstacles. Ainsi, nos contributions soutiennent la conception sûre et sécurisée des systèmes embarqués. / The presence of communicating embedded systems/IoTs in our daily lives have brought a myriad of benefits, from adding conveniences and entertainment, to improving the safety of our commutes and health care. However, the flaws and vulnerabilities in these devices expose their users to risks of property damage, monetary losses, and personal injury. For example, consumer vehicles, both connected and conventional, have succumbed to a variety of design flaws resulting in injuries and death. At the same time, as vehicles are increasingly connected (and in the near future, autonomous), researchers have demonstrated possible hacks on their sensors or internal control systems, including direct injection of messages on the CAN bus.Ensuring the safety of users or bystanders involves considering multiple factors. Conventional safety suggests that a system should not contain software and hardware flaws which can prevent it from correct function. `Safety of the Intended Function' involves avoiding the situations which the system or its components cannot handle, such as adverse extreme environmental conditions. Timing can be critical for certain real-time systems, as the system will need to respond to certain events, such as obstacle avoidance, within a set period to avoid dangerous situations. Finally, the safety of a system depends on its security. An attacker who can send custom commands or modify the software of the system may change its behavior and send it into various unsafe situations. Various safety and security countermeasures for embedded systems, especially connected vehicles, have been proposed. To place these countermeasures correctly requires methods of analyzing and verifying that the system meets all safety, security, and performance requirements, preferably at the early design phases to minimize costly re-work after production. This thesis discusses the safety and security considerations for embedded systems, in the context of Institut Vedecom's autonomous vehicle. Among the proposed approaches to ensure safety and security in embedded systems, Model-Driven Engineering is one such approach that covers the full design process, from elicitation of requirements, design of hardware and software, simulation/formal verification, and final code generation. This thesis proposes a modeling-based methodology for safe and secure design, based on the SysML-Sec Methodology, which involve new modeling and verification methods. Security modeling is generally performed in the last phases of design. However, security impacts the early architecture/mapping and HW/SW partitioning decisions should be made based on the ability of the architecture to satisfy security requirements. This thesis proposes how to model the security mechanisms and the impact of an attacker as relevant to the HW/SW Partitioning phase. As security protocols negatively impact performance, it becomes important to measure both the usage of hardware components and response times of the system. Overcharged components can result in unpredictable performance and undesired delays. This thesis also discusses latency measurements of safety-critical events, focusing on one critical to autonomous vehicles: braking as after obstacle detection. Together, these additions support the safe and secure design of embedded systems.

No description available.

With the improvements of the vehicle technology related with connectivity, sharing, automation and electrification and as a solution to the problems that cities are facing, such as an intense population growth and pollution, there are new forms of mobility that are or will be created within the framework of the future mobility. In this context, the arrival of driverless autonomous vehicles will provoke an irreversible change supporting the implementation of new forms of mobility or improving the existent. One factor that will help to do feasible the improvement of the existent mobility is the reduction of costs due to the arrival of autonomous vehicles, what will make ondemand transportation competitive under certain circumstances when comparing costs between it and fixed route systems. This thesis studies for the case of the metro/rail corridors in the metropolitan area of Stockholm which areas are suitable to implement Demand Responsive Transport (DRT) according to urban configuration and access to transit parameters. Once the identification is done, a model to compare between two different operating strategies for feeder services is applied to obtain which one is optimal under different stages of development of the technology related with the vehicles in the fields of automation and electrification. The model used, with additions to existing ones to adapt it to the use of it to real scenarios, gives numerical results for the four considered stages, showing the importance of the travel demand and the street sinuosity on the results and selection of the optimal. The method and criteria developed contributes to have a clear identification of the areas in which the implementation of the DRT services would be feasible in a future mobility scheme.

Autonomous vehicles, commonly known as “self-driving cars”, are increasingly becoming of interest for researchers due to their potential to mitigate traffic accidents and congestion. Using reinforcement learning, previous research has demonstrated that a DQN agent can be trained to effectively navigate a simulated two-lane environment via cooperative driving, in which a model of V2X technology allows an AV to receive information from surrounding vehicles (termed Primary Perceived Vehicles) to make driving decisions. Results have demonstrated that the DQN agent can learn to navigate longitudinally and laterally, but with a prohibitively high collision rate of 1.5% - 4.8% and an average speed of 13.4 m/s. In this research, the impact of including information from traffic vehicles that are outside of those that immediately surround the AV (termed Secondary Perceived Vehicles) as inputs to a DQN agent is investigated. Results indicate that while including velocity and distance information from SPVs does not improve the collision rate and average speed of the driving algorithm, it does yield a lower standard deviation of speed during episodes, indicating lower acceleration. This effect, however, is lost when the agent is tested under constant traffic flow scenarios (as opposed to fluctuating driving conditions). Taken together, it is concluded that while the SPV inclusion does not have an impact on collision rate and average speed, its ability to achieve the same performance with lower acceleration can significantly improve fuel economy and drive quality. These findings give a better understanding of how additional vehicle information during cooperative driving affects automated driving. / Thesis / Master of Applied Science (MASc)

This paper proposes a model to ensure safe and realistic human-robot interaction for an autonomous vehicle interacting with a human-driven vehicle, by incorporating the driving style of the human driver. The interaction is modeled as a game, where both agents try to maximize future rewards. The driving style of the human is captured via the role of the human driver in the game, capturing the fact that humans with different driving styles reason differently. The solution of the game is obtained using an numerical approximation and used by the autonomous vehicle to plan optimally ahead. The model is validated via simulations on a safety-critical scenario, where realistic driving style-dependent behaviour emerges naturally.

The state of the art of autonomous vehicles requires operators to remain vigilant while performing secondary tasks. The goal of this research was to investigate how dynamically allocated secondary tasks affected driving performance, cognitive load, and situation awareness. Secondary tasks were presented at rates based on the autonomy level present and whether the autonomous system was engaged. A rapid secondary task rate was also presented for two short periods regardless of whether autonomy was engaged. There was a three-minute familiarization phase followed by a data collection phase where participants responded to secondary tasks while preventing the vehicle from colliding into random obstacles. After data collection, there was a brief survey to gather data on cognitive load, situation awareness, and relevant demographics. The data was compared to data gathered in a similar study by Cossitt [10] where secondary tasks were presented at a controlled frequency and a gradually increasing frequency.

No description available.