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007			cr nn 008mamaa
008			220601s2020 sz | o |||| 0|eng d
020			_a9783031015069
024	7		_a10.1007/978-3-031-01506-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTL152.8 _b2020 EB
100	1		_aCao, Haotian _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686100
245	1	0	_aDecision Making, Planning, and Control Strategies for Intelligent Vehicles _cby Haotian Cao, Mingjun Li, Song Zhao, Xiaolin Song
250			_a1st edition 2020
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XII, 128 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Advances in Automotive Technology _x2576-8131
505	0		_aAcknowledgments -- Introduction -- Decision Making for Intelligent Vehicles -- Path and Speed Planning for Intelligent Vehicles -- Robust Trajectory Tracking Methods for Intelligent Vehicles -- Control Strategies for Human-Automation Cooperative Driving Systems -- Bibliography -- Authors' Biographies .
520			_aThe intelligent vehicle will play a crucial and essential role in the development of the future intelligent transportation system, which is developing toward the connected driving environment, ultimate driving safety, and comforts, as well as green efficiency. While the decision making, planning, and control are extremely vital components of the intelligent vehicle, these modules act as a bridge, connecting the subsystem of the environmental perception and the bottom-level control execution of the vehicle as well. This short book covers various strategies of designing the decision making, trajectory planning, and tracking control, as well as share driving, of the human-automation to adapt to different levels of the automated driving system. More specifically, we introduce an end-to-end decision-making module based on the deep Q-learning, and improved path-planning methods based on artificial potentials and elastic bands which are designed for obstacle avoidance. Then, the optimal method based on the convex optimization and the natural cubic spline is presented. As for the speed planning, planning methods based on the multi-object optimization and high-order polynomials, and a method with convex optimization and natural cubic splines, are proposed for the non-vehicle-following scenario (e.g., free driving, lane change, obstacle avoidance), while the planning method based on vehicle-following kinematics and the model predictive control (MPC) is adopted for the car-following scenario. We introduce two robust tracking methods for the trajectory following. The first one, based on nonlinear vehicle longitudinal or path-preview dynamic systems, utilizes the adaptive sliding mode control (SMC) law which can compensate for uncertainties to follow the speed or path profiles. The second one is based on the five-degrees-of-freedom nonlinear vehicle dynamical system that utilizes the linearized time-varying MPC to track the speed and path profile simultaneously. Toward human-automation cooperative driving systems, we introduce two control strategies to address the control authority and conflict management problems between the human driver and the automated driving systems. Driving safety field and game theory are utilized to propose a game-based strategy, which is used to deal with path conflicts during obstacle avoidance. Driver's driving intention, situation assessment, and performance index are employed for the development of the fuzzy-based strategy. Multiple case studies and demos are included in each chapter to show the effectiveness of the proposed approach. We sincerely hope the contents of this short book provide certain theoretical guidance and technical supports for the development of intelligent vehicle technology.
988			_aSynthesis Collection of Technology_2020
650		7	_2embne _9673285 _aAutomóviles _xControl automático
650		7	_2embne _9147646 _aSistemas de comunicación móviles
650		7	_2embne _9139914 _aTransportes _xModelos matemáticos
700	1		_aLi, Mingjun _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686101
700	1		_aZhao, Song _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686102
700	1		_aSong, Xiaolin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686103
776	0	8	_iPrinted edition: _z9783031000102
776	0	8	_iPrinted edition: _z9783031003783
776	0	8	_iPrinted edition: _z9783031026348
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01506-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eb _zSI