Results 151 to 160 of about 3,137 (204)

DAF-Aided ISAC Spatial Scattering Modulation for Multi-Hop V2V Networks. [PDF]

Sensors (Basel)
Fan Y, Wu J, Guo Y, Yang J, Zhao L, Yan W, Yang S, Ma H, Zhu C.   +8 more
europepmc   +1 more source

Prevention of Motorcycle-Car Door Collisions by Using a Deep-Learning-Based Automatic Braking Assistance System. [PDF]

Sensors (Basel)
Shiao Y, Huynh TL.
europepmc   +1 more source

An Approach to Modeling and Developing Virtual Sensors Used in the Simulation of Autonomous Vehicles. [PDF]

Sensors (Basel)
Barabás I, Iclodean C, Beles H, Antonya C, Molea A, Scurt FB.   +5 more
europepmc   +1 more source

Simulation assessment of autonomous electric vehicles on urban sustainability in Riyadh City. [PDF]

Sci Rep
Louati A, Louati H, Kariri E.
europepmc   +1 more source

Decentralized graph attention multi-agent reinforcement learning for adaptive urban traffic routing. [PDF]

Sci Rep
Mahmoud M, Meshoul S, Batouche M, Hammad M.   +3 more
europepmc   +1 more source

SGE-Flow: 4D mmWave Radar 3D Object Detection via Spatiotemporal Geometric Enhancement and Inter-Frame Flow. [PDF]

Sensors (Basel)
Meng H, Yu Z, Li C, Li C, Liu X.
europepmc   +1 more source

Multi-Frequency GPR Image Fusion Based on Convolutional Sparse Representation to Enhance Road Detection. [PDF]

J Imaging
Fang L, Yang F, Fang Y, Nie J.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

On-Road Vehicle Detection and Tracking Using MMW Radar and Monovision Fusion

IEEE Transactions on Intelligent Transportation Systems, 2016
With the potential to increase road safety and provide economic benefits, intelligent vehicles have elicited a significant amount of interest from both academics and industry. A robust and reliable vehicle detection and tracking system is one of the key modules for intelligent vehicles to perceive the surrounding environment.
Hongbin Sun, , Jingmin Xin
exaly   +2 more sources

On-road vehicle detection fusing radar and vision

Proceedings of 2011 IEEE International Conference on Vehicular Electronics and Safety, 2011
This paper presents a cross-verification approach to fuse radar and vision data for vehicle detection. Firstly, a realtime vision approach using specific shadow segmentation is used to detect vehicles in whole image independently. The fusion approach contains two steps: matching and validation. The targets respectively from radar and vision verify each
Hangen He
exaly   +2 more sources