Providing VANET Security Through Active Position Detection

Our main contribution is a novel approach to enhancing position security in VANET. We achieve local and global position security by using the on-board radar to detect neighboring vehicles and to confirm their announced coordinates. We compute cosine similarity among data collected by radar and neighbors\u27 reports to filter the forged data from the truthful data. Based on filtered data, we create a history of vehicle movement. By checking the history and computing similarity, we can prevent a large number of Sybil attacks and some combinations of Sybil and position-based attacks

Developing Adaptive Cruise Control Based on Fuzzy Logic Using Hardware Simulation

Ride comfort on the highway often interrupted because drivers need to adjust the vehicle speed. Safe distance between vehicles should be maintained is the main reason. The situation of monotonous and high speed will increase the risk of accidents on highway. A device is required by the driver to adjust the vehicle speed during the long distance (cruise) driving on highway without neglecting the safety aspects. The device is known as Adaptive Cruise Control (ACC). The ACC is a subsystem of Advanced Driver Assistance Systems (ADASs) that serves to assist the driver during cruise driving. The working principle of the ACC is the vehicle speed set automatically so that the distance to the vehicle in front remains safe. This paper presents the development of fuzzy logic controller for ACC. The fuzzy inference method used in this study is Mamdani. The result from hardware simulation that using remote control car shows that the fuzzy logic controller can work according to the design.DOI:http://dx.doi.org/10.11591/ijece.v4i6.673

Система управления движением автомобиля

Розроблена структурна схема і алгоритм роботи активної лазерної системи детектування, здатна розпізнавати дорогу та об’єкти, що на ній знаходяться, працездатний в різні періоди доби та в різних погодних умовах. Запропоновані та розроблені алгоритми зменшення швидкості та підготовки автомобіля до виконання повороту чи зміни напрямку руху з врахуванням неоднорідності покриття та безпечних параметрів руху. Синтезовані та змодельовані закони оптимального керування рухом автомобіля, досліджена їх точність та стабільність при впливі шумів і зміні параметрів системи. Синтезована оптимальна по критерію точності і швидкодії система автоматичного керування і навігаційна система для безпечного руху автомобіля.A Mathematical model of an automobile in motion is developed. The safety parameters of movement in complex and critical conditions such as bypassing an obstacle, turning or moving on a road with dissimilar surfaces are determined. The structure and algorithm of an active detecting laser system, with the ability to recognize the road and objects on the road during different times of the day and different weather conditions is developed. The algorithms to reduce the speed of the automobile to take a turn or to change the direction taking into account the dissimilarity of the road surface and safety parameters of motion are presented. The sensors that receive information of the automobile movement are selected; their accuracy and stability under the effect of noise and distortion of the system parameters are studied. The effect of the inertia of the measuring instruments on the whole control system is evaluated. The Avtomatic control system and navigation system for movement of the automobile safely based on accuracy and fast response is synthesized.Разработана структурная схема и алгоритм работы активной лазерной системы детектирования, способной распознавать дорогу и находящиеся на ней объекты, работоспособной в разное время суток и при любых погодных условиях. Предложены и разработаны алгоритмы уменьшения скорости и подготовки автомобиля к выполнению поворота или изменению направления движения с учетом неоднородности покрытия и безопасных параметров движения. Синтезированы и промоделированы законы оптимального управления движением автомобиля, исследована их точность и стабильность при воздействии шумов и изменении параметров системы. Оценено влияние на динамику системы инерционности блоков. Синтезирована оптимальная по критерию точности и быстродействия система автоматического управления и навигационная система для безопасного движения автомобиля

Game Theoretic Model Predictive Control for Autonomous Driving

This study presents two closely-related solutions to autonomous vehicle control problems in highway driving scenario using game theory and model predictive control. We first develop a game theoretic four-stage model predictive controller (GT4SMPC). The controller is responsible for both longitudinal and lateral movements of Subject Vehicle (SV) . It includes a Stackelberg game as a high level controller and a model predictive controller (MPC) as a low level one. Specifically, GT4SMPC constantly establishes and solves games corresponding to multiple gaps in front of multiple-candidate vehicles (GCV) when SV is interacting with them by signaling a lane change intention through turning light or by a small lateral movement. SV’s payoff is the negative of the MPC’s cost function , which ensures strong connection between the game and that the solution of the game is more likely to be achieved by a hybrid MPC (HMPC). GCV’s payoff is a linear combination of the speed payoff, headway payoff and acceleration payoff. . We use decreasing acceleration model to generate our prediction of TV’s future motion, which is utilized in both defining TV’s payoffs over the prediction horizon in the game and as the reference of the MPC. Solving the games gives the optimal gap and the target vehicle (TV). In the low level , the lane change process are divided into four stages: traveling in the current lane, leaving current lane, crossing lane marking, traveling in the target lane. The division identifies the time that SV should initiate actual lateral movement for the lateral controller and specifies the constraints HMPC should deal at each step of the MPC prediction horizon. Then the four-stage HMPC controls SV’s actual longitudinal motion and execute the lane change at the right moment. Simulations showed the GT4SMPC is able to intelligently drive SV into the selected gap and accomplish both discretionary land change (DLC) and mandatory lane change (MLC) in a dynamic situation. Human-in-the-loop driving simulation indicated that GT4SMPC can decently control the SV to complete lane changes with the presence of human drivers. Second, we propose a differential game theoretic model predictive controller (DGTMPC) to address the drawbacks of GT4SMPC. In GT4SMPC, the games are defined as table game, which indicates each players only have limited amount of choices for a specific game and such choice remain fixed during the prediction horizon. In addition, we assume a known model for traffic vehicles but in reality drivers’ preference is partly unknown. In order to allow the TV to make multiple decisions within the prediction horizon and to measure TV’s driving style on-line, we propose a differential game theoretic model predictive controller (DGTMPC). The high level of the hierarchical DGTMPC is the two-player differential lane-change Stackelberg game. We assume each player uses a MPC to control its motion and the optimal solution of leaders’ MPC depends on the solution of the follower. Therefore, we convert this differential game problem into a bi-level optimization problem and solves the problem with the branch and bound algorithm. Besides the game, we propose an inverse model predictive control algorithm (IMPC) to estimate the MPC weights of other drivers on-line based on surrounding vehicle’s real-time behavior, assuming they are controlled by MPC as well. The estimation results contribute to a more appropriate solution to the game against driver of specific type. The solution of the algorithm indicates the future motion of the TV, which can be used as the reference for the low level controller. The low level HMPC controls both the longitudinal motion of SV and his real-time lane decision. Simulations showed that the DGTMPC can well identify the weights traffic vehicles’ MPC cost function and behave intelligently during the interaction. Comparison with level-k controller indicates DGTMPC’s Superior performance

Model-based control for automotive applications

The number of distributed control systems in modern vehicles has increased exponentially over the past decades. Today’s performance improvements and innovations in the automotive industry are often resolved using embedded control systems. As a result, a modern vehicle can be regarded as a complex mechatronic system. However, control design for such systems, in practice, often comes down to time-consuming online tuning and calibration techniques, rather than a more systematic, model-based control design approach. The main goal of this thesis is to contribute to a corresponding paradigm shift, targeting the use of systematic, model-based control design approaches in practice. This implies the use of control-oriented modeling and the specification of corresponding performance requirements as a basis for the actual controller synthesis. Adopting a systematic, model-based control design approach, as opposed to pragmatic, online tuning and calibration techniques, is a prerequisite for the application of state-of-the-art controller synthesis methods. These methods enable to achieve guarantees regarding robustness, performance, stability, and optimality of the synthesized controller. Furthermore, from a practical point-of-view, it forms a basis for the reduction of tuning and calibration effort via automated controller synthesis, and fulfilling increasingly stringent performance demands. To demonstrate these opportunities, case studies are defined and executed. In all cases, actual implementation is pursued using test vehicles and a hardware-in-the-loop setup. • Case I: Judder-induced oscillations in the driveline are resolved using a robustly stable drive-off controller. The controller prevents the need for re-tuning if the dynamics of the system change due to wear. A hardware-in-the-loop setup, including actual sensor and actuator dynamics, is used for experimental validation. • Case II: A solution for variations in the closed-loop behavior of cruise control functionality is proposed, explicitly taking into account large variations in both the gear ratio and the vehicle loading of heavy duty vehicles. Experimental validation is done on a heavy duty vehicle, a DAF XF105 with and without a fully loaded trailer. • Case III: A systematic approach for the design of an adaptive cruise control is proposed. The resulting parameterized design enables intuitive tuning directly related to comfort and safety of the driving behavior and significantly reduces tuning effort. The design is validated on an Audi S8, performing on-the-road experiments. • Case IV: The design of a cooperative adaptive cruise control is presented, focusing on the feasibility of implementation. Correspondingly, a necessary and sufficient condition for string stability is derived. The design is experimentally tested using two Citroën C4’s, improving traffic throughput with respect to standard adaptive cruise control functionality, while guaranteeing string stability of the traffic flow. The case studies consider representative automotive control problems, in the sense that typical challenges are addressed, being variable operating conditions and global performance qualifiers. Based on the case studies, a generic classification of automotive control problems is derived, distinguishing problems at i) a full-vehicle level, ii) an in-vehicle level, and iii) a component level. The classification facilitates a characterization of automotive control problems on the basis of the required modeling and the specification of corresponding performance requirements. Full-vehicle level functionality focuses on the specification of desired vehicle behavior for the vehicle as a whole. Typically, the required modeling is limited, whereas the translation of global performance qualifiers into control-oriented performance requirements can be difficult. In-vehicle level functionality focuses on actual control of the (complex) vehicle dynamics. The modeling and the specification of performance requirements are typically influenced by a wide variety of operating conditions. Furthermore, the case studies represent practical application examples that are specifically suitable to apply a specific set of state-of-the-art controller synthesis methods, being robust control, model predictive control, and gain scheduling or linear parameter varying control. The case studies show the applicability of these methods in practice. Nevertheless, the theoretical complexity of the methods typically translates into a high computational burden, while insight in the resulting controller decreases, complicating, for example, (online) fine-tuning of the controller. Accordingly, more efficient algorithms and dedicated tools are required to improve practical implementation of controller synthesis methods

Game Theoretic Model Predictive Control for Autonomous Driving

This study presents two closely-related solutions to autonomous vehicle control problems in highway driving scenario using game theory and model predictive control. We first develop a game theoretic four-stage model predictive controller (GT4SMPC). The controller is responsible for both longitudinal and lateral movements of Subject Vehicle (SV) . It includes a Stackelberg game as a high level controller and a model predictive controller (MPC) as a low level one. Specifically, GT4SMPC constantly establishes and solves games corresponding to multiple gaps in front of multiple-candidate vehicles (GCV) when SV is interacting with them by signaling a lane change intention through turning light or by a small lateral movement. SV’s payoff is the negative of the MPC’s cost function , which ensures strong connection between the game and that the solution of the game is more likely to be achieved by a hybrid MPC (HMPC). GCV’s payoff is a linear combination of the speed payoff, headway payoff and acceleration payoff. . We use decreasing acceleration model to generate our prediction of TV’s future motion, which is utilized in both defining TV’s payoffs over the prediction horizon in the game and as the reference of the MPC. Solving the games gives the optimal gap and the target vehicle (TV). In the low level , the lane change process are divided into four stages: traveling in the current lane, leaving current lane, crossing lane marking, traveling in the target lane. The division identifies the time that SV should initiate actual lateral movement for the lateral controller and specifies the constraints HMPC should deal at each step of the MPC prediction horizon. Then the four-stage HMPC controls SV’s actual longitudinal motion and execute the lane change at the right moment. Simulations showed the GT4SMPC is able to intelligently drive SV into the selected gap and accomplish both discretionary land change (DLC) and mandatory lane change (MLC) in a dynamic situation. Human-in-the-loop driving simulation indicated that GT4SMPC can decently control the SV to complete lane changes with the presence of human drivers. Second, we propose a differential game theoretic model predictive controller (DGTMPC) to address the drawbacks of GT4SMPC. In GT4SMPC, the games are defined as table game, which indicates each players only have limited amount of choices for a specific game and such choice remain fixed during the prediction horizon. In addition, we assume a known model for traffic vehicles but in reality drivers’ preference is partly unknown. In order to allow the TV to make multiple decisions within the prediction horizon and to measure TV’s driving style on-line, we propose a differential game theoretic model predictive controller (DGTMPC). The high level of the hierarchical DGTMPC is the two-player differential lane-change Stackelberg game. We assume each player uses a MPC to control its motion and the optimal solution of leaders’ MPC depends on the solution of the follower. Therefore, we convert this differential game problem into a bi-level optimization problem and solves the problem with the branch and bound algorithm. Besides the game, we propose an inverse model predictive control algorithm (IMPC) to estimate the MPC weights of other drivers on-line based on surrounding vehicle’s real-time behavior, assuming they are controlled by MPC as well. The estimation results contribute to a more appropriate solution to the game against driver of specific type. The solution of the algorithm indicates the future motion of the TV, which can be used as the reference for the low level controller. The low level HMPC controls both the longitudinal motion of SV and his real-time lane decision. Simulations showed that the DGTMPC can well identify the weights traffic vehicles’ MPC cost function and behave intelligently during the interaction. Comparison with level-k controller indicates DGTMPC’s Superior performance

A finite state machine framework for robust analysis and control of hybrid systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 107-115).Hybrid systems, describing interactions between analog and discrete dynamics, are pervasive in engineered systems and pose unique, challenging performance verification and control synthesis problems. Existing approaches either lead to computationally intensive and sometimes undecidable problems, or make use of highly specialized discrete abstractions with questionable robustness properties. The thesis addresses some of these challenges by developing a systematic, computationally tractable approach for design and certification of systems with discrete, finite-valued actuation and sensing. This approach is inspired by classical robust control, and is based on the use of finite state machines as nominal models of the hybrid systems. The development does not assume a particular algebraic or topological structure on the signal sets. The thesis adopts an input/output view of systems, proposes specific classes of inequality constraints to describe performance objectives, and presents corresponding 'small gain' type arguments for robust performance verification. A notion of approximation that is compatible with the goal of controller synthesis is defined. An approximation architecture that is capable of handling unstable systems is also proposed.(cont.) Constructive algorithms for generating finite state machine approximations of the hybrid systems of interest, and for efficiently computing a-posteriori bounds on the approximation error are presented. Analysis of finite state machine models, which reduces to searching for an appropriate storage function, is also shown to be related to the problem of checking for the existence of negative cost cycles in a network, thus allowing for a verification algorithm with polynomial worst-case complexity. Synthesis of robust control laws is shown to reduce to solving a discrete, infinite horizon min-max problem. The resulting controllers consist of a finite state machine state observer for the hybrid system and a memoryless full state feedback switching control law. The use of this framework is demonstrated through a simple benchmark example, the problem of stabilizing a double integrator using switched gain feedback and binary sensing. Finally, some extensions to incremental performance objectives and robustness measures are presented.by Danielle C. Tarraf.Ph.D