Computer Vision enabled Adaptive Speed Limit Control for Vehicle Safety

Author(s):  
Abhi Lad ◽  
Prithviraj Kanaujia ◽  
Soumya ◽  
Yash Solanki
2007 ◽  
Vol 8 (1) ◽  
pp. 108-120 ◽  
Author(s):  
Mohan Manubhai Trivedi ◽  
Tarak Gandhi ◽  
Joel McCall

2014 ◽  
Vol 136 (8) ◽  
Author(s):  
Jaekwan Shin ◽  
Ikjin Lee

This paper presents a reliability-based analysis of road vehicle accidents and the optimization of roadway radius and speed limit design based on vehicle dynamics, mainly focusing on windy environments. The performance functions are formulated as failure modes of vehicle rollover and sideslip and are defined on a finite set of basic variables with probabilistic characteristics, so-called random variables. The random variables are vehicle speed, steer angle, tire–road friction coefficient, road bank angle, and wind speed. The probability of accident was evaluated using the first-order reliability method (FORM) and numerical studies were conducted using a single-unit truck model. The analysis demonstrates that wind is a significant factor when assessing vehicle safety on roads, and probabilistic studies such as reliability-based design optimization (RBDO) are necessarily required to enhance vehicle safety in windy environments. Accordingly, design optimization of roadway radius and speed limit was conducted, and new designs were proposed satisfying the target reliability. This study suggests that probabilistic mechanics and theory can be of value for analysis and design of wind-related vehicle safety.


1985 ◽  
Vol 30 (1) ◽  
pp. 47-47
Author(s):  
Herman Bouma
Keyword(s):  

1983 ◽  
Vol 2 (5) ◽  
pp. 130
Author(s):  
J.A. Losty ◽  
P.R. Watkins

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.


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