“Ease of Driving” Road Classification for Night-time Driving Conditions 2016-01-0119
This paper is an extension of our previous work on the CHASE (Classification by Holistic Analysis of Scene Environment) algorithm, that automatically classifies the driving complexity of a road scene image during day-time conditions and assigns it an ‘Ease of Driving’ (EoD) score. At night, apart from traffic variations and road type conditions, illumination changes are a major predominant factor that affect the road visibility and the driving easiness. In order to resolve the problem of analyzing the driving complexity of roads at night, a brightness detection module is incorporated in our end-to-end nighttime EoD system, which computes the ‘brightness factor’ (bright or dark) for that given night-time road scene. The brightness factor along with a multi-level machine learning classifier is then used to classify the EoD score for a night-time road scene. Our end-to-end ‘Night-time EoD system’ is a real-time onboard system implemented and tested on road scene data collected in Japan. We have improved the scope of the CHASE algorithm for computing EoD score for night-time driving conditions by including a brightness detection module.
Citation: Pillai, P., Yalla, V., and Oguchi, K., "“Ease of Driving” Road Classification for Night-time Driving Conditions," SAE Technical Paper 2016-01-0119, 2016, https://doi.org/10.4271/2016-01-0119. Download Citation
Author(s):
Preeti J. Pillai, Veeraganesh Yalla, Kentaro Oguchi
Affiliated:
Toyota InfoTechnology Center
Pages: 11
Event:
SAE 2016 World Congress and Exhibition
ISSN:
0148-7191
e-ISSN:
2688-3627
Related Topics:
Machine learning
Roads and highways
Mathematical models
Visibility
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