TY - GEN
T1 - A CNN-based path trajectory prediction approach with safety constraints
AU - Zaman, Mostafa
AU - Zohrabi, Nasibeh
AU - Abdelwahed, Sherif
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Safety is one of the essential aspects to be considered in operating self-driving vehicles. In this paper, we aim to detect lanes and vehicles from input video or image by implementing advanced image thresholding techniques to detect lanes. We also use a linear support vector machine (SVM) classifier to detect vehicles from the image or video. A convolutional neural network (CNN) uses the images with the specified lane and vehicle detection as an input. It predicts the intention of going right, left, or drive straight based on the relative car distance on the video or the image. The main idea is to integrate CNN with lane and vehicle detection modules to estimate a safety path progression for a specific amount of time from the video or image based on the relative distance from other vehicles. Simulation results are given to illustrate the effectiveness of the proposed approach.
AB - Safety is one of the essential aspects to be considered in operating self-driving vehicles. In this paper, we aim to detect lanes and vehicles from input video or image by implementing advanced image thresholding techniques to detect lanes. We also use a linear support vector machine (SVM) classifier to detect vehicles from the image or video. A convolutional neural network (CNN) uses the images with the specified lane and vehicle detection as an input. It predicts the intention of going right, left, or drive straight based on the relative car distance on the video or the image. The main idea is to integrate CNN with lane and vehicle detection modules to estimate a safety path progression for a specific amount of time from the video or image based on the relative distance from other vehicles. Simulation results are given to illustrate the effectiveness of the proposed approach.
UR - http://www.scopus.com/inward/record.url?scp=85096527363&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85096527363&partnerID=8YFLogxK
U2 - 10.1109/ITEC48692.2020.9161731
DO - 10.1109/ITEC48692.2020.9161731
M3 - Conference contribution
AN - SCOPUS:85096527363
T3 - 2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020
SP - 267
EP - 272
BT - 2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Transportation Electrification Conference and Expo, ITEC 2020
Y2 - 23 June 2020 through 26 June 2020
ER -