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lihexuan管理員
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奧地利-TU Graz-2023年車輛工程智能駕駛方向招博士生
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學(xué)校介紹: 格拉茨技術(shù)大學(xué)tu graz（奧地利），位于奧地利第二大城市、施蒂利亞州首府格拉茨。車輛工程及內(nèi)燃機(jī)技術(shù)屬于該學(xué)校的優(yōu)勢學(xué)科，汽車行業(yè)top級別的供應(yīng)商麥格納magna以及技術(shù)咨詢公司avl的總部都位于此。并且汽車芯片企業(yè)英飛凌，nxp，汽車動力電池供應(yīng)商三星sdi歐洲研發(fā)總部都位于此。相對于車輛工程專業(yè)學(xué)生是一個比較理想的目標(biāo)城市。 博士課題： Ego vehicle state estimation and target motion prediction 背景描述: In a bilateral research project with China, involving Tongji University, Wenzhou University along with Austrian and Chinese industry partners, a hardware demonstration of an electric autonomous shuttle bus involving collaborative perception will be developed. In the project TU Graz will contribute to develop and validate automated vehicle control. The optimum motion planned within the automated vehicle depends on the anticipated motion of other traffic participants in the near. Specifically, the probability of others e.g. to cut in in front of the vehicle or to brake will influence the motion planning and decision making. Another factor is the dynamic state of the bus itself, especially the vehicle’s side-slip angle, is an important measure for the vehicle’s stability and thus the safety. This is especially true for public transport where occupants are not required to wear seatbelts and may be in standing position during transport. It is a prerequisite for motion planning when deciding on braking, accelerating and steering interventions. PhD工作內(nèi)容: • Motion prediction of passenger cars described by probabilities to change lane, brake or accelerate using observations of the vehicle behavior in the past (when entering the observed area) and assigning this to different driver types. Data from traffic flow simulation will be used to develop and test these models. The predictions of the target traffic motion including probabilities of these predictions will be made available in the emulated cloud. • Side-slip angle estimation approach in Matlab/Simulink using both in-vehicle dynamic sensors (steering angle, accelerations, yaw rate wheel speeds, etc.) from the individual bus as well as the in-vehicle environmental sensors (camera, radar and/or lidar) using state observer. The observer will be tested mainly with simulated data from the combined simulation of traffic flow and multi-body simulation. 能力要求: • Master in Mechanical, Electrical/Electronic Engineering or Informatics • Expertise in control theory, parameter identification and multi-body simulation is welcome • Expertise in machine and deep learning is welcome • Excellent Programming skills 聯(lián)系方式: Ass.-Prof. Dr. Cornelia Lex, cornelia.lex@tugraz.at |
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