Advanced Sensing and Control for Connected and Automated Vehicles

• Format: eBook
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Subjects: History of engineering & technology; Technology: general issues; analytic hierarchy architecture; articulated cargo trucks; artificial neural networks; attention; attention feature fusion; autonomous driving; autonomous vehicle; autonomous vehicles; collision warning system; connected and autonomous vehicles; data-driven; dead reckoning; electric vehicle; electroencephalogram; end-to-end learning; executive control; fronto-parietal network; in-vehicle network; kabsch algorithm; kalman filter; model predictive control; multi-scale channel attention; multi-task learning; multiple-model; n/a; object detection; object vehicle estimation; off-tracking; path planning; potential field; radar accuracy; radar latency; real-time control; roll stability; sigmoid curve; simulated driving; simulation; string stability; task-cuing experiment; time to collision; traffic scenes; trajectory tracking; TROOP; truck platooning; tyre blow-out; ultra-wideband; unified chassis control; Unscented Kalman Filter; unsprung mass; urban platooning; urban vehicle platooning; V2V communication; vehicle dynamic parameters; vehicle dynamics model; vehicle-to-vehicle communication; weighted interpolation; yaw stability
• Online Access: Open Access: DOAB: description of the publication; Open Access: DOAB, download the publication
• Physical Description: 1 online resource (284 p.)
• ISBN: 9783036534879; 9783036534886; books978-3-0365-3488-6
• Access: Open Access

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.