Obstacle detection of rail transit based on deep learning

Measurement ◽  
2021 ◽  
Vol 176 ◽  
pp. 109241
Author(s):  
Deqiang He ◽  
Zhiheng Zou ◽  
Yanjun Chen ◽  
Bin Liu ◽  
Xiaoyang Yao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Obstacle Detection ◽  
Rail Transit

scholarly journals Computer vision based obstacle detection and target tracking for autonomous vehicles

2021 ◽  
Vol 336 ◽  
pp. 07004
Author(s):  
Ruoyu Fang ◽  
Cheng Cai
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Target Tracking ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Pid Algorithm ◽  
Deep Learning Neural Network

Obstacle detection and target tracking are two major issues for intelligent autonomous vehicles. This paper proposes a new scheme to achieve target tracking and real-time obstacle detection of obstacles based on computer vision. ResNet-18 deep learning neural network is utilized for obstacle detection and Yolo-v3 deep learning neural network is employed for real-time target tracking. These two trained models can be deployed on an autonomous vehicle equipped with an NVIDIA Jetson Nano motherboard. The autonomous vehicle moves to avoid obstacles and follow tracked targets by camera. Adjusting the steering and movement of the autonomous vehicle according to the PID algorithm during the movement, therefore, will help the proposed vehicle achieve stable and precise tracking.

scholarly journals Obstacle Detection and Safely Navigate the Autonomous Vehicle from Unexpected Obstacles on the Driving Lane

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4719
Author(s):  
Malik Haris ◽  
Jin Hou
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicle ◽  
Research Area ◽  
Visual Navigation ◽  
Obstacle Detection ◽  
The Road ◽  
Markov Random ◽  
Active Research ◽  
On The Road ◽  
Active Research Area

Nowadays, autonomous vehicle is an active research area, especially after the emergence of machine vision tasks with deep learning. In such a visual navigation system for autonomous vehicle, the controller captures images and predicts information so that the autonomous vehicle can safely navigate. In this paper, we first introduced small and medium-sized obstacles that were intentionally or unintentionally left on the road, which can pose hazards for both autonomous and human driving situations. Then, we discuss Markov random field (MRF) model by fusing three potentials (gradient potential, curvature prior potential, and depth variance potential) to segment the obstacles and non-obstacles into the hazardous environment. Since the segment of obstacles is done by MRF model, we can predict the information to safely navigate the autonomous vehicle form hazardous environment on the roadway by DNN model. We found that our proposed method can segment the obstacles accuracy from the blended background road and improve the navigation skills of the autonomous vehicle.

scholarly journals Prediction of Passenger Flow in Urban Rail Transit Based on Big Data Analysis and Deep Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 142272-142279 ◽  
Author(s):  
Kaer Zhu ◽  
Ping Xun ◽  
Wei Li ◽  
Zhen Li ◽  
Ruochong Zhou
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Passenger Flow ◽  
Urban Rail

Rail Transit Obstacle Detection Based on Improved CNN

2021 ◽  
Vol 70 ◽  
pp. 1-14
Author(s):  
Deqiang He ◽  
Zhiheng Zou ◽  
Yanjun Chen ◽  
Bin Liu ◽  
Jian Miao
Keyword(s):  
Obstacle Detection ◽  
Rail Transit

scholarly journals Prediction of Daily Entrance and Exit Passenger Flow of Rail Transit Stations by Deep Learning Method

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Huaizhong Zhu ◽  
Xiaoguang Yang ◽  
Yizhe Wang
Keyword(s):  
Deep Learning ◽  
Public Transportation ◽  
Influence Factors ◽  
Activation Function ◽  
Atmospheric Temperature ◽  
Weather Data ◽  
Rail Transit ◽  
Learning Method ◽  
Passenger Flow ◽  
Elevated Road

The prediction of entrance and exit passenger flow of rail transit stations is one of key research focuses in the area of intelligent transportation. Based on the big data of rail transit IC card (Public Transportation Card), this paper analyzes the data of major dynamic factors having effect on entrance passenger flow and exit passenger flow of rail transit stations: weather data, atmospheric temperature data, holiday and festival data, ground index data, and elevated road data and calculates the daily entrance passenger flow and daily exit passenger flow of individual rail transit stations with data reduction. Furthermore, based on the history data of passenger flow of rail transit stations and relevant influence factors, it applies the deep learning method to choose the relatively optimal hidden layer node by means of the cut-and-try method, set up input data and labeled data, select the activation function and loss function, and use the Adam Gradient Descent Optimization Algorithm for iterative global convergence. The results verify that this method accurately predicts the daily entrance passenger flow and daily exit passenger flow of rail transit stations with the prediction error of less than 4.1%. Finally, the proposed model is compared with the linear regression model.

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