Results 21 to 30 of about 470,864 (273)
Scene Understanding for Autonomous Driving
To detect and segment objects in images based on their content is one of the most active topics in the field of computer vision. Nowadays, this problem can be addressed using Deep Learning architectures such as Faster R-CNN or YOLO, among others. In this paper, we study the behaviour of different configurations of RetinaNet, Faster R-CNN and Mask R-CNN
Òscar Lorente, Ian Riera, Aditya Rana
openaire +2 more sources
Packet Inter-Reception Time Conditional Density Estimation Based on Surrounding Traffic Distribution
Cooperation is an enabler for autonomous vehicles. A promising application of cooperative driving is high-density platooning, where trucks drive with low inter-vehicle distances.
Guillaume Jornod +2 more
doaj +1 more source
Physics of Autonomous Driving based on Three-Phase Traffic Theory
We have revealed physical features of autonomous driving in the framework of the three-phase traffic theory for which there is no fixed time headway to the preceding vehicle.
Kerner, Boris S.
core +1 more source
Development Status and Hotspot Visualized Analysis of Autonomous Vehicles Based on CiteSpace
The safety of autonomous driving has been a constant concern, with autonomous driving becoming a research focus in the world today. This study collects the literature in the field of autonomous driving in the Web of Science core data collection from 2005
Lixin Yan +3 more
doaj +1 more source
TiEV: The Tongji Intelligent Electric Vehicle in the Intelligent Vehicle Future Challenge of China
TiEV is an autonomous driving platform implemented by Tongji University of China. The vehicle is drive-by-wire and is fully powered by electricity. We devised the software system of TiEV from scratch, which is capable of driving the vehicle autonomously ...
Cai, Lewen +18 more
core +1 more source
A Model-Based Spatio-Temporal Behavior Decider for Autonomous Driving
Spatio-temporal planning has emerged as a robust methodology for solving trajectory planning challenges in complex autonomous driving scenarios. By integrating both spatial and temporal variables, this approach facilitates the generation of highly ...
Yiwen Huang +5 more
doaj +1 more source
Deep Reinforcement Learning framework for Autonomous Driving
Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes.
Abdou, Mohammed +3 more
core +1 more source
Road traffic crashes caused more than 108,000 deaths and 6,200,000 injuries resulting in 7.7 million disability-adjusted life years (DALYs) lost in the Association of Southeast Asian Nations (ASEAN) in 2019.
Husam Muslim +6 more
doaj +1 more source
Intelligent Environment Enabling Autonomous Driving
Automated driving is expected to enormously evolve the transportation industry and ecosystems. Advancement in communications and sensor technologies have further accelerated the realization process of the autonomous driving goals.
Manzoor Ahmed Khan
doaj +1 more source
Editorial: Brain-inspired autonomous driving [PDF]
Elishai Ezra Tsur +2 more
doaj +2 more sources

