Decision making, planning, and control strategies for intelligent vehicles / Haotian Cao, Mingjun Li, Song Zhao, Xiaolin Son.Material type: TextSeries: Synthesis digital library of engineering and computer science | Synthesis lectures on advances in automotive technology ; #12.Publisher: San Rafael, California (1537 Fourth Street, 1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, Description: 1 PDF (xi, 126 pages) : illustrations (some color)Content type: text Media type: electronic Carrier type: online resourceISBN: 9781681738833Subject(s): Motor vehicles -- Automatic control | Intelligent transportation systems | Intelligent control systems | intelligent vehicle | decision making | path planning | speed planning | robust trajectory tracking control | driving intention | human-automation cooperative driving | deep Q-learning | driving hazard potential | convex optimization | dynamical game theoryGenre/Form: Electronic books.Additional physical formats: Print version:: No titleDDC classification: 629.23 LOC classification: TL152.8 $b.C364 2020ebOnline resources: Abstract with links to full text | Abstract with links to resource Also available in print.
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Mode of access: World Wide Web.
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Part of: Synthesis digital library of engineering and computer science.
Includes bibliographical references (pages 115-123).
1. Introduction -- 1.1. Brief introductions on trajectory planning for intelligent vehicles -- 1.2. Brief introductions on trajectory following control -- 1.3. Human-automation cooperative driving -- 1.4. Summary
2. Decision making for intelligent vehicles -- 2.1. Introduction -- 2.2. Decision-making methods -- 2.3. Decision making by deep q-learning -- 2.4. Summary
3. Path and speed planning for intelligent vehicles -- 3.1. Premiere -- 3.2. Path planning with elastic bands -- 3.3. Path planning with harmonic potentials for obstacle avoidance -- 3.4. Optimal path planning with natural cubic splines -- 3.5. Speed planning under the non-following scenario -- 3.6. Speed planning in the case of car following -- 3.7. Conclusions
4. Robust trajectory tracking methods for intelligent vehicles -- 4.1. Longitudinal velocity-tracking controller -- 4.2. Robust SMC steering controller -- 4.3. Trajectory tracking by the linearized time-varying MPC -- 4.4. Summary
5. Control strategies for human-automation cooperative driving systems -- 5.1. Driving intention recognition -- 5.2. Situation assessment -- 5.3. Game-based cooperative driving strategy -- 5.4. Fuzzy-based cooperative driving strategy -- 5.5. Summary.
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
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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.
Also available in print.
Title from PDF title page (viewed on July 30, 2020).