Browse Topic: Communication systems

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ABSTRACT A simulation capable of modeling grid-tied electrical systems, vehicle-to-grid (V2G) and vehicle-to-vehicle(V2V) resource sharing was developed within the MATLAB/Simulink environment. Using the steady state admittance matrix approach, the unknown currents and voltages within the network are determined at each time step. This eliminates the need for states associated with the distributed system. Each vehicle has two dynamic states: (1) stored energy and (2) fuel consumed while the generators have only a single fuel consumed state. One of its potential uses is to assess the sensitivity of fuel consumption with respect to the control system parameters used to maintain a vehicle-centric bus voltage under dynamic loading conditions.
Jane, Robert S.Parker, Gordon G.Weaver, Wayne W.Goldsmith, Steven Y.
This document describes [motor] vehicle driving automation systems that perform part or all of the dynamic driving task (DDT) on a sustained basis. It provides a taxonomy with detailed definitions for six levels of driving automation, ranging from no driving automation (Level 0) to full driving automation (Level 5), in the context of [motor] vehicles (hereafter also referred to as “vehicle” or “vehicles”) and their operation on roadways: Level 0: No Driving Automation Level 1: Driver Assistance Level 2: Partial Driving Automation Level 3: Conditional Driving Automation Level 4: High Driving Automation Level 5: Full Driving Automation These level definitions, along with additional supporting terms and definitions provided herein, can be used to describe the full range of driving automation features equipped on [motor] vehicles in a functionally consistent and coherent manner. “On-road” refers to publicly accessible roadways (including parking areas and private campuses that permit
On-Road Automated Driving (ORAD) committee
With the development of cellular communication technology and for the sake of reducing drag resistance, the multi-lane platoon technology will be more prosperous in the future. In this article, the cooperative vehicle platoon method on the public road is represented. The method’s architecture is mainly composed of the following parts: decision-making, path planning and control command generation. The decision-making uses the finite state machine to make decision and judgment on the cooperative lane change of vehicles, and starts to execute the lane change step when the lane change requirements are met. In terms of path planning, with the goal of ensuring comfort, the continuity of the vehicle state and no collision between vehicles, a fifth-order polynomial is used to fit every vehicle trajectory. In terms of control command generation module, a model predictive control algorithm is used to solve the multi-vehicle centralized optimization control problem. We use the two DOF vehicle
Chen, GuoshengWu, JianLi, ShuaiZhang, JinghuaDu, ZhiqiangWang, GuojunChen, Zhicheng
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