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240202 ||| eng |
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|a 9783036597669
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|a books978-3-0365-9767-6
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|a 9783036597676
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| 100 |
1 |
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|a Li, Bai
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| 245 |
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0 |
|a Recent Advances in Motion Planning and Control of Autonomous Vehicles
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2023
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| 300 |
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|a 1 electronic resource (224 p.)
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| 653 |
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|a Transport technology and trades / bicssc
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| 653 |
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|a joint dispatching and planning
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| 653 |
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|a brain-computer interface (BCI)
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| 653 |
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|a hierarchical framework
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| 653 |
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|a Hybrid A-star
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| 653 |
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|a autonomous driving
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| 653 |
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|a scheduling
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| 653 |
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|a n/a
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| 653 |
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|a improved A* algorithm
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| 653 |
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|a automated vehicle
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| 653 |
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|a nonlinear model predictive control
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| 653 |
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|a time delay neural network
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| 653 |
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|a data-driven control
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| 653 |
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|a History of engineering and technology / bicssc
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| 653 |
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|a RRT* algorithm
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| 653 |
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|a obstacle avoidance
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| 653 |
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|a numerical optimal control
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| 653 |
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|a adaptive model predictive control
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| 653 |
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|a spatial exploration
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| 653 |
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|a articulated tracked vehicle
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| 653 |
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|a GIS
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| 653 |
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|a path tracking
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| 653 |
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|a Technology: general issues / bicssc
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| 653 |
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|a dynamic programming
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| 653 |
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|a infrared positioning
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| 653 |
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|a cooperative trajectory planning
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| 653 |
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|a artificial neural network
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| 653 |
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|a autonomous forklift
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| 653 |
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|a deep reinforcement learning
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| 653 |
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|a kinematics
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| 653 |
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|a occlusion-aware path planning
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| 653 |
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|a articulated vehicles
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| 653 |
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|a tube model predictive control
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| 653 |
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|a intrinsic motivation
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| 653 |
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|a autonomous truck
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| 653 |
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|a path planning
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| 653 |
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|a eye-tracking
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| 653 |
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|a narrow corridor scene
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| 653 |
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|a space discretization strategy
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| 653 |
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|a autonomous underwater vehicle
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| 653 |
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|a drift control
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| 653 |
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|a open-pit mine
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| 653 |
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|a autonomous vehicle
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| 653 |
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|a quadratic program
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| 653 |
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|a threatening pedestrians
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| 653 |
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|a trajectory planning
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| 653 |
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|a artificial bee colony algorithm
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| 653 |
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|a unmanned driving
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| 653 |
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|a Hybrid A* search algorithm
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| 653 |
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|a trajectory tracking
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| 653 |
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|a electroencephalography (EEG)
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| 653 |
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|a steady-state visual evoked potential (SSVEP)
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| 653 |
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|a underground intelligent vehicles
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| 700 |
1 |
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|a Zhang, Youmin
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| 700 |
1 |
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|a Li, Xiaohui
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| 700 |
1 |
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|a Acarman, Tankut
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 024 |
8 |
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|a 10.3390/books978-3-0365-9767-6
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/132432
|z DOAB: description of the publication
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| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/8469
|7 0
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 900
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| 082 |
0 |
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|a 380
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| 082 |
0 |
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|a 700
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| 082 |
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|a 600
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| 082 |
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|a 620
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|a Autonomous vehicles are increasingly prevalent, navigating both structured urban roads and challenging offroad scenes. At the core of these vehicles lie the planning and control modules, which are crucial for demonstrating the intelligence inherent in an autonomous driving system. The planning module is responsible for devising an open-loop trajectory, taking into account a variety of environmental restrictions, task-related demands, and vehicle-kinematics-related constraints, while the control module ensures adherence to this trajectory in a closed-loop manner. This adherence is vital in a range of conditions, including diverse weather scenarios, different driving situations, and in response to potential disturbances such as mechanical failures or cyber threats. In certain contexts, these modules are collectively referred to as 'control', with the planning component considered an open-loop controller. This Special Issue focuses on the latest research trends in planning and control methods for autonomous driving. It comprises 11 papers that cover a broad spectrum of applications, including occlusion-aware motion planning in warehouses, control strategies for articulated vehicles, cooperative trajectory planning for autonomous forklifts, and tracking control for underwater vehicles in the face of disturbances and uncertainties. These contributions collectively underscore the diverse and evolving nature of autonomous vehicle technology.
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