A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

One major concern in the development of intelligent vehicles is to improve the driving safety. It is also an essential issue for future autonomous driving and intelligent transportation. In this paper, we present a vision-based system for driving assistance. A front and a rear on-board camera are ad...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 20; no. 18; p. 5139
Main Authors: Lin, Huei-Yung, Dai, Jyun-Min, Wu, Lu-Ting, Chen, Li-Qi
Format: Journal Article
Language:English
Published: Basel MDPI AG 09.09.2020
MDPI
Subjects:
advanced driver assistance system
Algorithms
Cameras
forward collision warning
Identification
Investigations
lane change detection
Neural networks
overtaking vehicle identification
Parameter estimation
Roads & highways
Sensors
Traffic accidents & safety
Vehicles
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:One major concern in the development of intelligent vehicles is to improve the driving safety. It is also an essential issue for future autonomous driving and intelligent transportation. In this paper, we present a vision-based system for driving assistance. A front and a rear on-board camera are adopted for visual sensing and environment perception. The purpose is to avoid potential traffic accidents due to forward collision and vehicle overtaking, and assist the drivers or self-driving cars to perform safe lane change operations. The proposed techniques consist of lane change detection, forward collision warning, and overtaking vehicle identification. A new cumulative density function (CDF)-based symmetry verification method is proposed for the detection of front vehicles. The motion cue obtained from optical flow is used for overtaking detection. It is further combined with a convolutional neural network to remove repetitive patterns for more accurate overtaking vehicle identification. Our approach is able to adapt to a variety of highway and urban scenarios under different illumination conditions. The experiments and performance evaluation carried out on real scene images have demonstrated the effectiveness of the proposed techniques.
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This paper is an extended version of Jyun-Min Dai; Lu-Ting Wu; Huei-Yung Lin and Wen-Lung Tai, A driving assistance system with vision based vehicle detection techniques. In Proceedings of the 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Jeju, Korea, 13–16 December 2016.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20185139