Publication Type:

Journal Article

Source:

Journal of Advanced Research in Dynamical and Control Systems, Institute of Advanced Scientific Research, Inc., Volume 10, Number 9 Special Issue, p.871-879 (2018)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050698803&partnerID=40&md5=d23e116ad1840fde0f8f76e3fb2efe31

Abstract:

<p>The current work aims at introducing the concept and suggesting a possible implementation methodology for achieving the following objectives, given a video containing vehicles under conditions of normal traffic: 1) Detection of target vehicle using deep learning 2) Trajectory complexity parameter (TCP) derivation. These objectives or phases achieved in sequence would result in TCP which is a parameter introduced to estimate the complexity of the trajectory of vehicle in front. The proposed method, employing Faster R-CNN (Regions with Convolutional Neural Networks) for detection of the vehicle and an x coordinate gradient based logic for deriving TCP, is tested for the feasibility of the concept. TCP is a representation of the driving pattern of the target vehicle’s driver. This measure has its potential usage in aspects like detecting rash drivers travelling in front of us and estimating conditions where more attention from the driver is required due to complex driving pattern of vehicles in front. © 2018, Institute of Advanced Scientific Research, Inc. All rights reserved.</p>

Notes:

cited By 0

Cite this Research Publication

A. M. Geetha and Dr. Senthil Kumar T., “Deep learning for driver assistance using estimated trajectory complexity parameter”, Journal of Advanced Research in Dynamical and Control Systems, vol. 10, pp. 871-879, 2018.