LEADER 04479nam 2201141z- 450 001 9910566481403321 005 20231214133346.0 035 $a(CKB)5680000000037566 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/81125 035 $a(EXLCZ)995680000000037566 100 $a20202205d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Sensing and Control for Connected and Automated Vehicles 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (284 p.) 311 $a3-0365-3487-3 311 $a3-0365-3488-1 330 $aConnected 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. 606 $aTechnology: general issues$2bicssc 606 $aHistory of engineering & technology$2bicssc 610 $aTROOP 610 $atruck platooning 610 $apath planning 610 $akalman filter 610 $aV2V communication 610 $astring stability 610 $aoff-tracking 610 $aarticulated cargo trucks 610 $akabsch algorithm 610 $apotential field 610 $asigmoid curve 610 $aautonomous vehicles 610 $aconnected and autonomous vehicles 610 $aartificial neural networks 610 $aend-to-end learning 610 $amulti-task learning 610 $aurban vehicle platooning 610 $asimulation 610 $aattention 610 $aexecutive control 610 $asimulated driving 610 $atask-cuing experiment 610 $aelectroencephalogram 610 $afronto-parietal network 610 $aobject vehicle estimation 610 $aradar accuracy 610 $adata-driven 610 $aradar latency 610 $aweighted interpolation 610 $aautonomous vehicle 610 $aurban platooning 610 $avehicle-to-vehicle communication 610 $ain-vehicle network 610 $aanalytic hierarchy architecture 610 $atraffic scenes 610 $aobject detection 610 $amulti-scale channel attention 610 $aattention feature fusion 610 $acollision warning system 610 $aultra-wideband 610 $adead reckoning 610 $atime to collision 610 $avehicle dynamic parameters 610 $aUnscented Kalman Filter 610 $amultiple-model 610 $aelectric vehicle 610 $aunified chassis control 610 $aunsprung mass 610 $aautonomous driving 610 $atrajectory tracking 610 $areal-time control 610 $amodel predictive control 610 $atyre blow-out 610 $ayaw stability 610 $aroll stability 610 $avehicle dynamics model 615 7$aTechnology: general issues 615 7$aHistory of engineering & technology 700 $aHuang$b Chao$4edt$01326262 702 $aDu$b Haiping$4edt 702 $aZhao$b Wanzhong$4edt 702 $aZhao$b Yifan$4edt 702 $aYan$b Fuwu$4edt 702 $aLv$b Chen$4edt 702 $aHuang$b Chao$4oth 702 $aDu$b Haiping$4oth 702 $aZhao$b Wanzhong$4oth 702 $aZhao$b Yifan$4oth 702 $aYan$b Fuwu$4oth 702 $aLv$b Chen$4oth 906 $aBOOK 912 $a9910566481403321 996 $aAdvanced Sensing and Control for Connected and Automated Vehicles$93037230 997 $aUNINA