|
|
|
|
| LEADER |
04299nma a2201069 u 4500 |
| 001 |
EB002043615 |
| 003 |
EBX01000000000000001187281 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
220822 ||| eng |
| 020 |
|
|
|a 9783036534886
|
| 020 |
|
|
|a 9783036534879
|
| 020 |
|
|
|a books978-3-0365-3488-6
|
| 100 |
1 |
|
|a Huang, Chao
|
| 245 |
0 |
0 |
|a Advanced Sensing and Control for Connected and Automated Vehicles
|h Elektronische Ressource
|
| 260 |
|
|
|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
|
| 300 |
|
|
|a 1 electronic resource (284 p.)
|
| 653 |
|
|
|a unsprung mass
|
| 653 |
|
|
|a V2V communication
|
| 653 |
|
|
|a collision warning system
|
| 653 |
|
|
|a ultra-wideband
|
| 653 |
|
|
|a kabsch algorithm
|
| 653 |
|
|
|a unified chassis control
|
| 653 |
|
|
|a dead reckoning
|
| 653 |
|
|
|a radar accuracy
|
| 653 |
|
|
|a vehicle dynamic parameters
|
| 653 |
|
|
|a autonomous driving
|
| 653 |
|
|
|a n/a
|
| 653 |
|
|
|a potential field
|
| 653 |
|
|
|a sigmoid curve
|
| 653 |
|
|
|a end-to-end learning
|
| 653 |
|
|
|a kalman filter
|
| 653 |
|
|
|a History of engineering & technology / bicssc
|
| 653 |
|
|
|a simulation
|
| 653 |
|
|
|a Technology: general issues / bicssc
|
| 653 |
|
|
|a yaw stability
|
| 653 |
|
|
|a fronto-parietal network
|
| 653 |
|
|
|a data-driven
|
| 653 |
|
|
|a object vehicle estimation
|
| 653 |
|
|
|a radar latency
|
| 653 |
|
|
|a vehicle-to-vehicle communication
|
| 653 |
|
|
|a roll stability
|
| 653 |
|
|
|a off-tracking
|
| 653 |
|
|
|a attention feature fusion
|
| 653 |
|
|
|a multiple-model
|
| 653 |
|
|
|a electric vehicle
|
| 653 |
|
|
|a object detection
|
| 653 |
|
|
|a executive control
|
| 653 |
|
|
|a model predictive control
|
| 653 |
|
|
|a real-time control
|
| 653 |
|
|
|a urban platooning
|
| 653 |
|
|
|a articulated cargo trucks
|
| 653 |
|
|
|a time to collision
|
| 653 |
|
|
|a path planning
|
| 653 |
|
|
|a connected and autonomous vehicles
|
| 653 |
|
|
|a simulated driving
|
| 653 |
|
|
|a analytic hierarchy architecture
|
| 653 |
|
|
|a autonomous vehicles
|
| 653 |
|
|
|a autonomous vehicle
|
| 653 |
|
|
|a task-cuing experiment
|
| 653 |
|
|
|a multi-scale channel attention
|
| 653 |
|
|
|a urban vehicle platooning
|
| 653 |
|
|
|a string stability
|
| 653 |
|
|
|a weighted interpolation
|
| 653 |
|
|
|a artificial neural networks
|
| 653 |
|
|
|a traffic scenes
|
| 653 |
|
|
|a trajectory tracking
|
| 653 |
|
|
|a vehicle dynamics model
|
| 653 |
|
|
|a Unscented Kalman Filter
|
| 653 |
|
|
|a tyre blow-out
|
| 653 |
|
|
|a truck platooning
|
| 653 |
|
|
|a in-vehicle network
|
| 653 |
|
|
|a attention
|
| 653 |
|
|
|a electroencephalogram
|
| 653 |
|
|
|a TROOP
|
| 653 |
|
|
|a multi-task learning
|
| 700 |
1 |
|
|a Du, Haiping
|
| 700 |
1 |
|
|a Zhao, Wanzhong
|
| 700 |
1 |
|
|a Zhao, Yifan
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b DOAB
|a Directory of Open Access Books
|
| 500 |
|
|
|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
|
| 028 |
5 |
0 |
|a 10.3390/books978-3-0365-3488-6
|
| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/81125
|z DOAB: description of the publication
|
| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/5154
|7 0
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 720
|
| 082 |
0 |
|
|a 900
|
| 082 |
0 |
|
|a 380
|
| 082 |
0 |
|
|a 700
|
| 082 |
0 |
|
|a 600
|
| 082 |
0 |
|
|a 620
|
| 520 |
|
|
|a Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans' body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs.
|