Road Parameter-Based Driver Assistance System for Safe Driving

Authors Abstract
Content
One of the key aspects of Advanced Driver Assistance Systems (ADAS) is ensuring the safety of the driver by maintaining a safe drivable speed. Overspeeding is one of the critical factors for accidents and vehicle rollovers, especially at road turns. This article aims to propose a driver assistance system for safe driving on Indian roads. In this regard, a camera-based classification of the road type combined with the road curvature estimation helps the driver to maintain a safe drivable speed primarily at road curves. Three Deep Convolutional Neural Network (CNN) models viz. Inception-v3, ResNet-50, and VGG-16 are being used for the task of road type classification. In this regard, the models are validated using a self-created dataset of Indian roads and an optimal performance of 83.2% correct classification is observed. For the calculation of road curvature, a lane tracking algorithm is used to estimate the curve radius of a structured road. The road type classification and the estimated road curvature values are given as inputs to a simulation-based model, CARSIM (vehicle road simulator to estimate the drivable speed). The recommended speed is then compared and analyzed with the actual speeds obtained from subjective tests.
Meta TagsDetails
DOI
https://doi.org/10.4271/12-02-04-0019
Pages
9
Citation
Addanki, S., Sowmya Vasuki, J., and Venkataraman, H., "Road Parameter-Based Driver Assistance System for Safe Driving," SAE Int. J. CAV 2(4):253-262, 2019, https://doi.org/10.4271/12-02-04-0019.
Additional Details
Publisher
Published
Dec 17, 2019
Product Code
12-02-04-0019
Content Type
Journal Article
Language
English