scholarly journals Occluded Street Objects Perception Algorithm of Intelligent Vehicles Based on 3D Projection Model

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Wei Liu ◽  
Longsheng Wei ◽  
Yongbo Li
Keyword(s):  
Augmented Reality ◽  
Affine Transformation ◽  
Evaluation Method ◽  
Intelligent Vehicles ◽  
Video Streams ◽  
Projection Model ◽  
Automatic Driving ◽  
Vehicle To Vehicle ◽  
Perception System ◽  
Assistant Systems

We present a super perception system of intelligent vehicles for perceiving occluded street objects by collecting images from neighbor front vehicles V2V (vehicle to vehicle) video streams based on 3D projection model. This super power can avoid some serious accidents of driver-assistant systems or automatic driving systems which can only detect visible objects. Our street perception system can “see through” the front vehicles to realize detecting of the occluded street objects only by analyzing the pair images received from front and host (back) vehicles. Upon the 3D projection model based on the pair images, the system uses affine transformation to achieve augmented reality method to increase the visibility perspective of driver system. Experimental results on different datasets are shown to validate our approach. Evaluation method was first introduced into our perception system.

scholarly journals Cooperative Visual Augmentation Algorithm of Intelligent Vehicle Based on Inter-Vehicle Image Fusion

2021 ◽  
Vol 11 (24) ◽  
pp. 11917
Author(s):  
Wei Liu ◽  
Yun Ma ◽  
Mingqiang Gao ◽  
Shuaidong Duan ◽  
Longsheng Wei
Keyword(s):  
Image Fusion ◽  
Urban Areas ◽  
Feature Matching ◽  
Intelligent Vehicles ◽  
Geometric Transformation ◽  
Connected Vehicle ◽  
Projection Model ◽  
Vehicle To Vehicle ◽  
Computer Vision Technology ◽  
Institute Of Technology

In a connected vehicle environment based on vehicle-to-vehicle (V2V) technology, images from front and ego vehicles are fused to augment a driver’s or autonomous system’s visual field, which is helpful in avoiding road accidents by eliminating the blind point (the objects occluded by vehicles), especially tailgating in urban areas. Realizing multi-view image fusion is a tough problem without knowing the relative location of two sensors and the fusing object is occluded in some views. Therefore, we propose an image geometric projection model and a new fusion method between neighbor vehicles in a cooperative way. Based on a 3D inter-vehicle projection model, selected feature matching points are adopted to estimate the geometric transformation parameters. By adding deep information, our method also designs a new deep-affine transformation to realize fusing of inter-vehicle images. Experimental results on KIITI (Karlsruhe Institute of Technology and Toyota Technological Institute) datasets are shown to validate our algorithm. Compared with previous work, our method improves the IoU index by 2~3 times. This algorithm can effectively enhance the visual perception ability of intelligent vehicles, and it will help to promote the further development and improvement of computer vision technology in the field of cooperative perception.

scholarly journals Deep Learning Based Data Fusion for Sensor Fault Diagnosis and Tolerance in Autonomous Vehicles

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Huihui Pan ◽  
Weichao Sun ◽  
Qiming Sun ◽  
Huijun Gao
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Evaluation Method ◽  
Environmental Perception ◽  
Intelligent Vehicles ◽  
Sensor Data ◽  
Perception System ◽  
Global And Local

AbstractEnvironmental perception is one of the key technologies to realize autonomous vehicles. Autonomous vehicles are often equipped with multiple sensors to form a multi-source environmental perception system. Those sensors are very sensitive to light or background conditions, which will introduce a variety of global and local fault signals that bring great safety risks to autonomous driving system during long-term running. In this paper, a real-time data fusion network with fault diagnosis and fault tolerance mechanism is designed. By introducing prior features to realize the lightweight network, the features of the input data can be extracted in real time. A new sensor reliability evaluation method is proposed by calculating the global and local confidence of sensors. Through the temporal and spatial correlation between sensor data, the sensor redundancy is utilized to diagnose the local and global confidence level of sensor data in real time, eliminate the fault data, and ensure the accuracy and reliability of data fusion. Experiments show that the network achieves state-of-the-art results in speed and accuracy, and can accurately detect the location of the target when some sensors are out of focus or out of order. The fusion framework proposed in this paper is proved to be effective for intelligent vehicles in terms of real-time performance and reliability.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

scholarly journals Supporting User Onboarding in Automated Vehicles through Multimodal Augmented Reality Tutorials

2021 ◽  
Vol 5 (5) ◽  
pp. 22
Author(s):  
Henrik Detjen ◽  
Robert Niklas Degenhart ◽  
Stefan Schneegass ◽  
Stefan Geisler
Keyword(s):  
Augmented Reality ◽  
Task Performance ◽  
Online Survey ◽  
Smartphone Application ◽  
Assistant Systems ◽  
Vehicle Automation ◽  
Hedonic Quality ◽  
First Time ◽  
Reducing Potential ◽  
And Task

Misconceptions of vehicle automation functionalities lead to either non-use or dangerous misuse of assistant systems, harming the users’ experience by reducing potential comfort or compromise safety. Thus, users must understand how and when to use an assistant system. In a preliminary online survey, we examined the use, trust, and the perceived understanding of modern vehicle assistant systems. Despite remaining incomprehensibility (36–64%), experienced misunderstandings (up to 9%), and the need for training (around 30%), users reported high trust in the systems. In the following study with first-time users, we examine the effect of different User Onboarding approaches for an automated parking assistant system in a Tesla and compare the traditional text-based manual with a multimodal augmented reality (AR) smartphone application in means of user acceptance, UX, trust, understanding, and task performance. While the User Onboarding experience for both approaches shows high pragmatic quality, the hedonic quality was perceived significantly higher in AR. For the automated parking process, reported hedonic and pragmatic user experience, trust, automation understanding, and acceptance do not differ, yet the observed task performance was higher in the AR condition. Overall, AR might help motivate proper User Onboarding and better communicate how to operate the system for inexperienced users.

VANET Routing and Trust Model Evaluation Using Artificial Bee Colony Algorithm

2017 ◽  
Vol 7 (7) ◽  
pp. 182
Author(s):  
. Rakhi ◽  
G. L Pahuja
Keyword(s):  
Ad Hoc ◽  
Intelligent Transportation System ◽  
Trust Model ◽  
Modern Technology ◽  
Intelligent Vehicles ◽  
Bee Colony ◽  
Vehicle To Vehicle ◽  
Trust Models ◽  
Near Future ◽  
Smart Vehicle

Vehicular ad-hoc network contains of very intelligent vehicles on the pathways and give communication service to the dives in the network or can connect with the roadside devices. In the near future it will provide many service and fast delivery of information with minimal delay. It is the modern technology which is mixing the wireless networking to vehicles. The main goal of the VANET system is to provide uninterrupted connectivity to the vehicular users on road, smart vehicle to vehicle interaction without any interruptions is known as intelligent transportation system (ITS). In this paper, we present a review on the VANET, its trust issues, how routing is done in VANET. Different routing and the type of trust models with which routing takes place are discussed. Comparison of parameter such as throughput, bit-error rate and delay are done on the basis of, with optimization and without optimization according to number of rounds.

Sign in / Sign up

Export Citation Format

Share Document