Abstract Modern economic growth has been based on mass industrial production and consumption, which have heavily relied on fossil fuels and energy waste since the 18th century. Hence, current socio-technical systems are unsustainable in meeting humans’ basic needs, such as energy and mobility. Fossil energy resources are non-renewable and, on the one hand, contribute to emissions that cause unreliable harm to the environment. In this research, the prime theory of Transformational Change illuminates how to use science and technology policy to meet social needs sustainably and inclusively in societies. This article answers questions regarding the essential policies and governance measures that states need to implement for the transition to electric Autonomous Vehicles (AVs) in the socio-technical system. Using meta-synthesis, followed by a case study and interviews with experts in the electric AV field, the article identifies state policies and governance measures to facilitate the transition of the sociotechnical system into electric AVs. The conceptualization of these roles determines that the state’s role is influenced by policy, governance, and legal decisions, which are ultimately implemented through specific combinations of policies.

In this study, the roll, yaw and depth fuzzy control of an Autonomous Underwater Vehicle (AUV) are addressed. Yaw and roll angles are regulated only using their errors and rates, but due to the complexity of depth dynamic channel, additional pitch rate quantity is used to improve the depth loop performance. The discussed AUV has four aps at the rear of the Vehicle as actuators. Two rule bases and membership functions based on Mamdani type and Sugeno type fuzzy rule have been chosen in each loop. By invoking the normalized steepest descent optimization method, the optimum values for the membership function parameters are found. Though the AUV is a highly nonlinear system, the simulation of the designed fuzzy logic control system based on the equations of motion shows desirable behavior of the AUV specially when the parameters of the fuzzy membership functions are optimized.

This paper introduces a trajectory planning algorithm for long-term freeway driving for Autonomous Vehicles including different modes of motion. In the Autonomous driving in a freeway, different maneuvers are needed, including free flow, distance adaption, speed adaption, lane change and overtaking. This paper introduces an algorithm that provides all of these driving scenarios in the trajectory planning for an Autonomous Vehicle. All maneuvers are classified and proper formulation for each driving mode formulated. Then, an algorithm is introduced to show the procedure of decision making and switching between all driving modes. The relative distances and velocities of the other peripheral and front Vehicle from Autonomous Vehicle are considered as the main factors for decision making during the travelling in the freeway. By the developed simulation programming, validity and effectiveness of the algorithm are verified, and pseudo code and flowchart for the simulation programming are introduced. Later in two simulation studies, different driving conditions are generated and results have been discussed and analyzed by detail.

In this paper, a new approach to optimize an Autonomous Underwater Vehicle (AUV) hull geometry is presented. Using this methode, the nose and tail of an underwater Vehicle are designed, such that their length constraints due to the arrangement of different components in the AUV body are properly addressed. In the current study, an optimal design for the body profile of a torpedo-shaped AUV is conducted, and a multi-objective optimization scheme based on the optimization algorithm Nondominated Sorting Genetic Algorithm-II (NSGA-II), as an evolutionary algorithm is employed. In addition, predefined geometrical constraints were considered so that equipment with the specific dimensions can be placed inside the AUV space without any effect on the AUV volume and the wetted surface. By optimizing the parameters of the newly presented profile, in addition to maximizing the volume and minimizing the wetted surface area, more diversed shapes can be achieved than with the ‘ Myring’ profile. A CFD analysis of the final optimal design indicates that with the help of the proposed profile, the hydrodynamic parameters for the AUV hull were effectively improved.

Today’s semi-Autonomous Vehicles are gradually moving towards full autonomy. This transition requires developing effective control algorithms for handing complex Autonomous tasks. Driving as a group of Vehicles, referred to as a convoy, on automated highways is a highly important and challenging task that Autonomous driving systems must deal with. This paper considers the control problem of a Vehicle convoy modeled with linear dynamics. The convoy formation requirement is presented in terms of a quadratic performance index to minimize. The convoy formation control is formulated as a receding horizon linear-quadratic (LQ) optimal control problem. The receding horizon control law is innovatively defined via the solution to the algebraic Riccati equation. The solution matrix and therefore the receding horizon control law are obtained in the closed-form. A control architecture consisting of four algorithms is proposed to handle formation size/shape switching. The closed-form control law is at the core of these algorithms. Simulation results are provided to justify the models, solutions, and proposed algorithms.

Nowadays Autonomous Underwater Vehicles (AUVs) are an unavoidable part of marine industries. One of the most important parts of any Autonomous Vehicle is the control issue to achieve the desired performance. This paper is concerned with speed control of an AUV model respecting the state and control constraints. According to the Newton-Euler method, the 6 DOF kinematic and dynamic models of the AUV are established. A well-defined performance index and constrained finite horizon optimization program in the form of Model Predictive Control (MPC) strategy is proposed to regulate the horizontal speed of AUV to its desired value while the constraints on the states like depth and control signals are considered in finite time horizon optimization program to be satisfied. The main problem for such a situation is the interaction between speed control and depth deviation then quadratic programing technique managed responses to avoid state and control signal constraints. Simulation results show a reliable performance of proposed MPC strategy to control the horizontal speed of AUV while all the constraints on state, control signal and also the variation of the control signal are satisfied.