05576nam 2201333z- 450 991058021400332120231214133514.0(CKB)5690000000011948(oapen)https://directory.doabooks.org/handle/20.500.12854/87529(EXLCZ)99569000000001194820202207d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierAdvances in Automated Driving SystemsBaselMDPI - Multidisciplinary Digital Publishing Institute20221 electronic resource (294 p.)3-0365-4503-4 3-0365-4504-2 Electrification, automation of vehicle control, digitalization and new mobility are the mega-trends in automotive engineering, and they are strongly connected. While many demonstrations for highly automated vehicles have been made worldwide, many challenges remain in bringing automated vehicles to the market for private and commercial use. The main challenges are as follows: reliable machine perception; accepted standards for vehicle-type approval and homologation; verification and validation of the functional safety, especially at SAE level 3+ systems; legal and ethical implications; acceptance of vehicle automation by occupants and society; interaction between automated and human-controlled vehicles in mixed traffic; human–machine interaction and usability; manipulation, misuse and cyber-security; the system costs of hard- and software and development efforts. This Special Issue was prepared in the years 2021 and 2022 and includes 15 papers with original research related to recent advances in the aforementioned challenges. The topics of this Special Issue cover: Machine perception for SAE L3+ driving automation; Trajectory planning and decision-making in complex traffic situations; X-by-Wire system components; Verification and validation of SAE L3+ systems; Misuse, manipulation and cybersecurity; Human–machine interactions, driver monitoring and driver-intention recognition; Road infrastructure measures for the introduction of SAE L3+ systems; Solutions for interactions between human- and machine-controlled vehicles in mixed traffic.Technology: general issuesbicsscHistory of engineering & technologybicsscautomated drivingscenario-based testingsoftware frameworktraffic signsADAStraffic sign recognition systemcooperative perceptionITSdigital twinsensor fusionedge cloudautonomous driftingmodel predictive control (MPC)successive linearizationadaptive controlvehicle motion controlvarying road surfacesvehicle dynamicsMask R-CNNtransfer learninginverse gamma correctionilluminationinstance segmentationpedestrian custom datasetdeep learningwheel loadersthrottle predictionstate predictionautomationsafety validationautomated driving systemsdecompositionmodular safety approvalmodular testingfault tree analysisadaptive cruise controlinformed machine learningphysics-guided reinforcement learningsafetyautonomous vehiclesautonomous conflict managementUTMUAVUGVU-Spaceframework developmentlane detectionsimulation and modellingmulti-layer perceptronconvolutional neural networkdriver drowsinessECG signalheart rate variabilitywavelet scalogramautomated driving (AD)driving simulatorexpression of trustacceptancesimulator case studyNASA TLXadvanced driver assistant systems (ADAS)system usability scaledriving schoolvirtual validationground truthreference measurementcalibration methodsimulationtraffic evaluationsimulation and modelingconnected and automated vehicledriver assistance systemvirtual test and validationradar sensorphysical perception modelvirtual sensor modelTechnology: general issuesHistory of engineering & technologyEichberger Arnoedt1328211Szalay ZsoltedtFellendorf MartinedtLiu HenryedtEichberger ArnoothSzalay ZsoltothFellendorf MartinothLiu HenryothBOOK9910580214003321Advances in Automated Driving Systems3038431UNINA