Advanced Sensing and Safety Control for Connected and Automated Vehicles
Connected and automated vehicles (CAVs) are a transformative technology expected to change and improve the safety and efficiency of mobile vehicles. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data,...
Sábháilte in:
| Formáid: | Online |
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| Teanga: | Béarla |
| Foilsithe / Cruthaithe: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Ábhair: | |
| Rochtain ar líne: | ONIX_20250812T095121_9783725826599_5 |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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| _version_ | 1869522923993169920 |
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| collection | Directory of Open Access Books |
| description | Connected and automated vehicles (CAVs) are a transformative technology expected to change and improve the safety and efficiency of mobile vehicles. 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 research hot spot in recent years. Thanks to improved sensing technologies, CAVs can interpret sensory information to detect obstacles, localize their positions, navigate, and interact with vehicles in the surrounding dynamic environment. Further, by leveraging computer vision and other sensing methods, these vehicles can recognize in-cabin human body activities, facial emotions, and mental states. |
| format | Online |
| id | doab-20.500.12854ir-165056 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1650562025-08-12T07:57:39Z Advanced Sensing and Safety Control for Connected and Automated Vehicles Huang, Chao Wang, Yafei Hang, Peng Zuo, Zhiqiang Leng, Bo Huang, Hailong model-free adaptive feedback sliding mode control path tracking autonomous vehicle recursive least squares forgetting factor Lyapunov stability autonomous vehicles multimodal driving datasets LiDAR driver biometric data air turbulence error CFD simulation multi-rotor UAVs meteorological observation vehicle anonymization IoVs pseudonym consumption adversary BSM traceability 4WID-4WIS EVs trajectory tracking control multi-objective coordinated control mutant particle swarm optimization (MPSO) vehicle localization Kalman filter ADAS kinematic model GPS TLA ITS system dynamics driverless vehicles future transportation transport policy smart mobility tracked vehicles engine output torque prediction model GA–BP neural network estimation of rolling resistance coefficient full-field sensing compressive sensing multi-agent system mobile sensors formation control structural health monitoring eco-driving ecological cooperative adaptive cruise control velocity trajectory dynamic programming traffic simulation testing edge case generation scenario-based testing parametric representation data-driven method takeover stabilization conditionally automated driving driving simulator user study physiology electric vehicle weight distribution active safety systems wheel slip controller torque vectoring thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general Connected and automated vehicles (CAVs) are a transformative technology expected to change and improve the safety and efficiency of mobile vehicles. 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 research hot spot in recent years. Thanks to improved sensing technologies, CAVs can interpret sensory information to detect obstacles, localize their positions, navigate, and interact with vehicles in the surrounding dynamic environment. Further, by leveraging computer vision and other sensing methods, these vehicles can recognize in-cabin human body activities, facial emotions, and mental states. 2025-08-12T07:57:36Z 2025-08-12T07:57:36Z 2025 book ONIX_20250812T095121_9783725826599_5 9783725826599 9783725826605 https://directory.doabooks.org/handle/20.500.12854/165056 eng image/jpeg Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/10605 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-2660-5 10.3390/books978-3-7258-2660-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725826599 9783725826605 264 open access |
| spellingShingle | model-free adaptive feedback sliding mode control path tracking autonomous vehicle recursive least squares forgetting factor Lyapunov stability autonomous vehicles multimodal driving datasets LiDAR driver biometric data air turbulence error CFD simulation multi-rotor UAVs meteorological observation vehicle anonymization IoVs pseudonym consumption adversary BSM traceability 4WID-4WIS EVs trajectory tracking control multi-objective coordinated control mutant particle swarm optimization (MPSO) vehicle localization Kalman filter ADAS kinematic model GPS TLA ITS system dynamics driverless vehicles future transportation transport policy smart mobility tracked vehicles engine output torque prediction model GA–BP neural network estimation of rolling resistance coefficient full-field sensing compressive sensing multi-agent system mobile sensors formation control structural health monitoring eco-driving ecological cooperative adaptive cruise control velocity trajectory dynamic programming traffic simulation testing edge case generation scenario-based testing parametric representation data-driven method takeover stabilization conditionally automated driving driving simulator user study physiology electric vehicle weight distribution active safety systems wheel slip controller torque vectoring thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general Advanced Sensing and Safety Control for Connected and Automated Vehicles |
| title | Advanced Sensing and Safety Control for Connected and Automated Vehicles |
| title_full | Advanced Sensing and Safety Control for Connected and Automated Vehicles |
| title_fullStr | Advanced Sensing and Safety Control for Connected and Automated Vehicles |
| title_full_unstemmed | Advanced Sensing and Safety Control for Connected and Automated Vehicles |
| title_short | Advanced Sensing and Safety Control for Connected and Automated Vehicles |
| title_sort | advanced sensing and safety control for connected and automated vehicles |
| topic | model-free adaptive feedback sliding mode control path tracking autonomous vehicle recursive least squares forgetting factor Lyapunov stability autonomous vehicles multimodal driving datasets LiDAR driver biometric data air turbulence error CFD simulation multi-rotor UAVs meteorological observation vehicle anonymization IoVs pseudonym consumption adversary BSM traceability 4WID-4WIS EVs trajectory tracking control multi-objective coordinated control mutant particle swarm optimization (MPSO) vehicle localization Kalman filter ADAS kinematic model GPS TLA ITS system dynamics driverless vehicles future transportation transport policy smart mobility tracked vehicles engine output torque prediction model GA–BP neural network estimation of rolling resistance coefficient full-field sensing compressive sensing multi-agent system mobile sensors formation control structural health monitoring eco-driving ecological cooperative adaptive cruise control velocity trajectory dynamic programming traffic simulation testing edge case generation scenario-based testing parametric representation data-driven method takeover stabilization conditionally automated driving driving simulator user study physiology electric vehicle weight distribution active safety systems wheel slip controller torque vectoring thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general |
| topic_facet | model-free adaptive feedback sliding mode control path tracking autonomous vehicle recursive least squares forgetting factor Lyapunov stability autonomous vehicles multimodal driving datasets LiDAR driver biometric data air turbulence error CFD simulation multi-rotor UAVs meteorological observation vehicle anonymization IoVs pseudonym consumption adversary BSM traceability 4WID-4WIS EVs trajectory tracking control multi-objective coordinated control mutant particle swarm optimization (MPSO) vehicle localization Kalman filter ADAS kinematic model GPS TLA ITS system dynamics driverless vehicles future transportation transport policy smart mobility tracked vehicles engine output torque prediction model GA–BP neural network estimation of rolling resistance coefficient full-field sensing compressive sensing multi-agent system mobile sensors formation control structural health monitoring eco-driving ecological cooperative adaptive cruise control velocity trajectory dynamic programming traffic simulation testing edge case generation scenario-based testing parametric representation data-driven method takeover stabilization conditionally automated driving driving simulator user study physiology electric vehicle weight distribution active safety systems wheel slip controller torque vectoring thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general |
| url | ONIX_20250812T095121_9783725826599_5 |