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Books > Professional & Technical > Energy technology & engineering > Electrical engineering
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Decision Making, Planning, and Control Strategies for Intelligent Vehicles (Paperback)
Loot Price: R1,690
Discovery Miles 16 900
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Decision Making, Planning, and Control Strategies for Intelligent Vehicles (Paperback)
Series: Synthesis Lectures on Advances in Automotive Technology
Expected to ship within 10 - 15 working days
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The 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.
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