Advances in Automated Driving Systems

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 t...

Full description

Saved in:
Bibliographic Details
HerausgeberIn:
Sonstige:
Year of Publication:2022
Language:English
Physical Description:1 electronic resource (294 p.)
Tags: Add Tag
No Tags, Be the first to tag this record!
LEADER 05462nam-a2201297z--4500
001 993545071104498
005 20231214133514.0
006 m o d
007 cr|mn|---annan
008 202207s2022 xx |||||o ||| 0|eng d
035 |a (CKB)5690000000011948 
035 |a (oapen)https://directory.doabooks.org/handle/20.500.12854/87529 
035 |a (EXLCZ)995690000000011948 
041 0 |a eng 
100 1 |a Eichberger, Arno  |4 edt 
245 1 0 |a Advances in Automated Driving Systems 
260 |a Basel  |b MDPI - Multidisciplinary Digital Publishing Institute  |c 2022 
300 |a 1 electronic resource (294 p.) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
506 |a Open access  |f Unrestricted online access  |2 star 
520 |a 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. 
546 |a English 
650 7 |a Technology: general issues  |2 bicssc 
650 7 |a History of engineering & technology  |2 bicssc 
653 |a automated driving 
653 |a scenario-based testing 
653 |a software framework 
653 |a traffic signs 
653 |a ADAS 
653 |a traffic sign recognition system 
653 |a cooperative perception 
653 |a ITS 
653 |a digital twin 
653 |a sensor fusion 
653 |a edge cloud 
653 |a autonomous drifting 
653 |a model predictive control (MPC) 
653 |a successive linearization 
653 |a adaptive control 
653 |a vehicle motion control 
653 |a varying road surfaces 
653 |a vehicle dynamics 
653 |a Mask R-CNN 
653 |a transfer learning 
653 |a inverse gamma correction 
653 |a illumination 
653 |a instance segmentation 
653 |a pedestrian custom dataset 
653 |a deep learning 
653 |a wheel loaders 
653 |a throttle prediction 
653 |a state prediction 
653 |a automation 
653 |a safety validation 
653 |a automated driving systems 
653 |a decomposition 
653 |a modular safety approval 
653 |a modular testing 
653 |a fault tree analysis 
653 |a adaptive cruise control 
653 |a informed machine learning 
653 |a physics-guided reinforcement learning 
653 |a safety 
653 |a autonomous vehicles 
653 |a autonomous conflict management 
653 |a UTM 
653 |a UAV 
653 |a UGV 
653 |a U-Space 
653 |a framework development 
653 |a lane detection 
653 |a simulation and modelling 
653 |a multi-layer perceptron 
653 |a convolutional neural network 
653 |a driver drowsiness 
653 |a ECG signal 
653 |a heart rate variability 
653 |a wavelet scalogram 
653 |a automated driving (AD) 
653 |a driving simulator 
653 |a expression of trust 
653 |a acceptance 
653 |a simulator case study 
653 |a NASA TLX 
653 |a advanced driver assistant systems (ADAS) 
653 |a system usability scale 
653 |a driving school 
653 |a virtual validation 
653 |a ground truth 
653 |a reference measurement 
653 |a calibration method 
653 |a simulation 
653 |a traffic evaluation 
653 |a simulation and modeling 
653 |a connected and automated vehicle 
653 |a driver assistance system 
653 |a virtual test and validation 
653 |a radar sensor 
653 |a physical perception model 
653 |a virtual sensor model 
776 |z 3-0365-4503-4 
776 |z 3-0365-4504-2 
700 1 |a Szalay, Zsolt  |4 edt 
700 1 |a Fellendorf, Martin  |4 edt 
700 1 |a Liu, Henry  |4 edt 
700 1 |a Eichberger, Arno  |4 oth 
700 1 |a Szalay, Zsolt  |4 oth 
700 1 |a Fellendorf, Martin  |4 oth 
700 1 |a Liu, Henry  |4 oth 
906 |a BOOK 
ADM |b 2023-12-15 05:56:09 Europe/Vienna  |f system  |c marc21  |a 2022-07-14 08:50:39 Europe/Vienna  |g false 
AVE |i DOAB Directory of Open Access Books  |P DOAB Directory of Open Access Books  |x https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5337823890004498&Force_direct=true  |Z 5337823890004498  |b Available  |8 5337823890004498