.png){kind=icon}
.png){kind=icon}
Return
Efficient Traffic Accident Warning Based on Unsupervised Prediction Framework
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3077120.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recognizing potentially hazardous objects is crucial in the field of transportation, especially in assisted and unmanned driving. However, most existing studies do not focus on defensive driving as they only identify accidents ahead of the vehicle in which they are not involved. In this paper, a driving assistance system is proposed to predict the risk score of potential targets ahead of the vehicle and provide an early warning, which relies on a deep architecture called Fusion-Residual Predictive Network (FRPN) that fused multi-scale residual features and improved adversarial learning. This architecture provides an environment for the generator to perform joint learning from ground-truth images and discriminators with gradient penalty constraints. The deeper convolutional neural network can greatly improve the quality of the image by fusing residual features. Several deep convolutional neural network models were used to evaluate the method on various datasets; among them, the prediction model based on the VGG network, with peak signal-to-noise ratio of 32.67 and structural similarity index of 0.921, respectively, yielded the best results. Subsequently, we utilize the tracking model to design a risk score evaluation method based on the location of the target and it have an improvement in ability to give early warning with 1.95s earlier in the best case. These results prove that our method can effectively reduce the risk of traffic accidents.
Keywords:
Accidents
Predictive models
Target tracking
Prediction algorithms
Generative adversarial networks
Dynamics
Automobiles
Generative adversarial network
video prediction
recurrent neural network
convolution neural network
object tracking
traffic warning
unsupervised learning
risk score assessment
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W