Assistive Devices for Safe Driving at a Crossing with No Traffic Lights Using 920 MHz Band

2019 ◽  
pp. 77-92
Author(s):  
Shintaro Uno
Keyword(s):  
Assistive Devices ◽  
Safe Driving ◽  
Traffic Lights

scholarly journals Performance Analysis of V2V and V2I LiFi Communication Systems in Traffic Lights

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Gerardo Hernandez-Oregon ◽  
Mario E. Rivero-Angeles ◽  
Juan C. Chimal-Eguía ◽  
Arturo Campos-Fentanes ◽  
Jorge G. Jimenez-Gallardo ◽  
...  
Keyword(s):  
Mobile Communications ◽  
Communication Systems ◽  
Vehicular Networks ◽  
Low Cost ◽  
Red Light ◽  
Green Light ◽  
Mathematical Expression ◽  
Traffic Light ◽  
Safe Driving ◽  
Traffic Lights

Vehicular networks is a key technology for efficiently communicating both user’s devices and cars for timely information regarding safe driving conditions and entertaining applications like social media, video streaming, and gaming services, among others. In view of this, mobile communications making use of cellular resources may not be an efficient and cost-effective alternative. In this context, the implementation of light-fidelity (LiFi) in vehicular communications could be a low-cost, high-data-rate, and efficient-bandwidth usage solution. In this work, we propose a mathematical analysis to study the average throughput in a road intersection equipped with a traffic light that operates as a server, which is assumed to have LiFi communication links with the front lights of the vehicles waiting for the green light. We further assume that the front vehicle (the car next to the traffic light) is able to communicate to the car immediately behind it by using its own tail lights and the front lights of such vehicle, and so on and so forth. The behavior of the road junction is modeled by a Markov chain, applying the Queueing theory with an M/M/1 system in order to obtain the average queue length. Then, Little’s theorem is applied to calculate the average waiting delay when the red light is present in the traffic light. Finally, the mathematical expression of the data throughput is derived.

Investigating Visual Recognition of Color and Safe Driving in Color-Weak Drivers

2021 ◽  
pp. 036119812110363
Author(s):  
Qingzhou Wang ◽  
Hongyu Wang ◽  
Te Luo ◽  
Lei Shi ◽  
Xin Fan
Keyword(s):  
Eye Movement ◽  
Color Vision ◽  
Recognition Test ◽  
Visual Recognition ◽  
Road Traffic ◽  
Error Rates ◽  
Dominant Factor ◽  
Normal Vision ◽  
Safe Driving ◽  
Traffic Lights

This study examined the visual characteristics of drivers with color weakness to improve their safety while driving. Significantly affected by the traffic environment, drivers with color weakness are not able to recognize traffic lights rapidly and accurately, which endangers traffic safety. In the first part of the research, through a static visual recognition test of color vision using the orthogonal method, the study explored the influence of light intensity, visual recognition distance, and color weakness type on the perception of traffic light colors by participants with color weakness. In the second part, a dynamic visual recognition test of color vision was conducted through simulating the driving environment of urban roads. Eye movement indexes between participants with color weakness and those with normal vision were analyzed by means of different vehicle speeds and time periods. The results indicated that the type of color weakness was the dominant factor affecting visual recognition of traffic lights. The eye movements of participants with deuteranomaly were close to those of people with normal vision, whereas the eye movement index of those with protanomaly and dyserythrochloropsia were significantly different. Distraction, slower responses, and higher color recognition error rates for the traffic lights were major characteristics—all representing risks that increase at night. To reduce the probability of road traffic injury, the driving safety of people with color weakness should be addressed.

Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

2018 ◽  
Author(s):  
George Symeonidis ◽  
Peter P. Groumpos ◽  
Evangelos Dermatas
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Lights ◽  
Adverse Conditions

Modeling Interstation Travel Speed of Hybrid Bus Rapid Transit within A Bayesian Framework

2019 ◽  
Vol 2 (5) ◽  
Author(s):  
Ji-hua Hu ◽  
Jia-xian Liang
Keyword(s):  
Road Traffic ◽  
Travel Speed ◽  
Bayesian Framework ◽  
Bus Rapid Transit ◽  
Rapid Transit ◽  
Important Indicator ◽  
Main Road ◽  
Traffic Lights ◽  
Speed Distribution ◽  
Hybrid Bus

Interstation travel speed is an important indicator of the running state of hybrid Bus Rapid Transit and passenger experience. Due to the influence of road traffic, traffic lights and other factors, the interstation travel speeds are often some kind of multi-peak and it is difficult to use a single distribution to model them. In this paper, a Gaussian mixture model charactizing the interstation travel speed of hybrid BRT under a Bayesian framework is established. The parameters of the model are inferred using the Reversible-Jump Markov Chain Monte Carlo approach (RJMCMC), including the number of model components and the weight, mean and variance of each component. Then the model is applied to Guangzhou BRT, a kind of hybrid BRT. From the results, it can be observed that the model can very effectively describe the heterogeneous speed data among different inter-stations, and provide richer information usually not available from the traditional models, and the model also produces an excellent fit to each multimodal speed distribution curve of the inter-stations. The causes of different speed distribution can be identified through investigating the Internet map of GBRT, they are big road traffic and long traffic lights respectively, which always contribute to a main road crossing. So, the BRT lane should be elevated through the main road to decrease the complexity of the running state.

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