NeurAll: Towards a Unified Visual Perception Model for Automated Driving
Convolutional Neural Networks (CNNs) are successfully used for the important automotive visual perception tasks including object recognition, motion and depth estimation, visual SLAM, etc.
Chennupati, Sumanth +6 more
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A dataset on the physiological state and behavior of drivers in conditionally automated driving. [PDF]
Data Brief, 2023 Meteier Q +8 more
europepmc +1 more source
A review on AI Safety in highly automated driving. [PDF]
Front Artif Intell, 2022 Wäschle M +4 more
europepmc +1 more source
Driving risk cognition of passengers in highly automated driving based on the prefrontal cortex activity via fNIRS. [PDF]
Sci Rep, 2023 Wang H +8 more
europepmc +1 more source
Effects of brand and brand trust on initial trust in fully automated driving system. [PDF]
PLoS One, 2023 Cui Z, Tu N, Itoh M.
europepmc +1 more source
Reliable and transparent in-vehicle agents lead to higher behavioral trust in conditionally automated driving systems. [PDF]
Front Psychol, 2023 Taylor S, Wang M, Jeon M.
europepmc +1 more source
Anticipated fear and anxiety of Automated Driving Systems: Estimating the prevalence in a national representative survey. [PDF]
Int J Clin Health Psychol, 2023 Meinlschmidt G +4 more
europepmc +1 more source
Automated driving regulations – where are we now?
Transportation Research Interdisciplinary PerspectivesTina Sever, Giuseppe Contissa
semanticscholar +1 more source
Examining the Effects of Visibility and Time Headway on the Takeover Risk during Conditionally Automated Driving. [PDF]
Int J Environ Res Public Health, 2022 Peng H, Chen F, Chen P.
europepmc +1 more source
Realising Meaningful Human Control Over Automated Driving Systems: A Multidisciplinary Approach. [PDF]
Minds Mach (Dordr), 2022 de Sio FS +5 more
europepmc +1 more source