Results 261 to 270 of about 95,328 (314)

Top-down norms and psychological empowerment: Explaining Chinese public acceptance of autonomous driving. [PDF]

PLoS One
Liao W, Liu X, Jiang K, Liu X, Yang J, Chen J.   +5 more
europepmc   +1 more source

LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving. [PDF]

Sensors (Basel)
Yang Y, Yang S, Chan Q.
europepmc   +1 more source

ACNet: An Attention-Convolution Collaborative Semantic Segmentation Network on Sensor-Derived Datasets for Autonomous Driving. [PDF]

Sensors (Basel)
Zhang Q, Hua K, Zhang Z, Zhao Y, Chen P.
europepmc   +1 more source

Insights of semantic segmentation using the DeepLab architecture for autonomous driving. [PDF]

MethodsX
Subhedar J, Bachute MR.
europepmc   +1 more source

Efficient and real-time perception: a survey on end-to-end event-based object detection in autonomous driving. [PDF]

Front Robot AI
Smagulova K, Elsheikh A, Silva DA, Fouda ME, Eltawil AM.   +4 more
europepmc   +1 more source

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SupportingTrust in Autonomous Driving

Proceedings of the 22nd International Conference on Intelligent User Interfaces, 2017
Autonomous cars will likely hit the market soon, but trust into such a technology is one of the big discussion points in the public debate. Drivers who have always been in complete control of their car are expected to willingly hand over control and blindly trust a technology that could kill them.
Renate Häuslschmid, Max von Bülow 0002, Bastian Pfleging, Andreas Butz   +3 more
openaire   +1 more source

Visual Evaluation for Autonomous Driving

IEEE Transactions on Visualization and Computer Graphics, 2022
Autonomous driving technologies often use state-of-the-art artificial intelligence algorithms to understand the relationship between the vehicle and the external environment, to predict the changes of the environment, and then to plan and control the behaviors of the vehicle accordingly.
Yijie Hou, Chengshun Wang, Junhong Wang, Xiangyang Xue 0001, Xiaolong Luke Zhang, Jun Zhu, Dongliang Wang, Siming Chen 0001   +7 more
openaire   +2 more sources

Autonomous driving in NMR

Magnetic Resonance in Chemistry, 2016
The automatic analysis of NMR data has been a much‐desired endeavour for the last six decades, as it is the case with any other analytical technique. This need for automation has only grown as advances in hardware; pulse sequences and automation have opened new research areas to NMR and increased the throughput of data.
openaire   +2 more sources

Multimedia for Autonomous Driving

IEEE MultiMedia, 2019
Multimedia has played an indispensable role in the success of various real-world applications, from smart healthcare to intelligent surveillance systems. In this new era of technology, one of these essential and useful applications is autonomous driving or self-driving cars.
openaire   +1 more source