"Ease of Driving" Road Classification for Night-time Driving Conditions Toyota InfoTechnology Center

Format:

Conference/Event

Author/Creator:

Pillai, Pillai, author.

Contributor:

Oguchi, Kentaro

Yalla, Veeraganesh

Conference Name:

SAE 2016 World Congress and Exhibition (2016-04-12 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2016

Summary:

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

Notes:

Vendor supplied data

Publisher Number:

2016-01-0119

Access Restriction:

Restricted for use by site license