recognition of highway workzones for reliable autonomous driving

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Recognition of highway work zones for reliable autonomous driving

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PRESENTED BY

DINU K JAMES S7 ECE ROLL NO 11

GUIDED BY CHRISLY ISSAC ASST PROF DEPT OF ECE

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Contents Introduction About autonomous carWorkzone detection and classificationExperiments and resultsConclusion

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Autonomous car What do you mean by Autonomous Car?The word autonomous is derived from a Greek roots. Autonomous = autos + nomos (SELF) (GOVERN)

So, Autonomous car is a vehicle that can drive itself from one point to another without assistance from a driver; in other words, with anautopilot system.

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Autonomous car What is the need of this type of car?Driver error is the most common cause of traffic accidents.

Great blessing to physically challenged people. Cell phones in-car ,entertainment systems, more traffic and more complicated road systems making it more frequent.

By this improving technology our car will do the concentrating for us.

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Autonomous car6

ChallengesUnexpected road conditions(workzones)Snowy conditions Traffic signal detection Legal issues High cost of manufacturing

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WORKZONE DETECTIONAims at identifying bounds of a work zoneUses computer vision algorithms, that could recognize work zone traffic signs.

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WORKZONE DETECTION

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Recognizing temporary highway changesI. SIGN DETECTIONII.SIGN TRACKINGIII.SIGN CLASSIFFICATION

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I.SIGN DETECTIONTWO METHODS:Color based methods

Shape based methods11

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Colour Based detectionUse color- value thresholding to detect the potential signs in an imageUses machine learning techniques to obtain the optimal thresholds of target color Performs binary pixel wise classification(Orange or Not Orange)Performs non maximal supression

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SIGN TRACKINGImproves the accuracy of sign detectionUses mean shift algorithm on the input image and identifies potential sign regions at multiple sub regions.Represents different image sub regions as candidate pdf.

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SIGN TRACKINGCompares the candidate pdf with target pdf and the candidate pdf with highest score is chosenThe output of the tracker is used as the target pdf for next image.The output of the tracker is then given to the classifier, which classifies the sign.17

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SIGN CLASSIFFICATION

The output from tracker is given as the input to the sign classification module.Removes the background.Takes the log polar transform of the input image.A predefined set of nine commonly used signs.Compares with predefined set and correctly classifies the sign image.20

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EXPERIMENTS & RESULTSCollected several hours of video footage of various driving experiencesSix videos were selected from them, that contained workzonesDetailed analysis was done results of recognition

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EXPERIMENTAims to demonstrate how this recognition system works effectively as a part of self driving car.A mock workzone is set up at the testing site100m long workzone in 400m long road.

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This test shows how a self driving car could:1)recognise the workzone2)lower its speed2)drive at normal speed after passing through the workzone.26

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ConclusionEvaluated some computer vision method used to localise,detect and classify workzone.Tested this system using several hours of video footages.Results were found to be satisfying.This work can be further extended to recognize and respond to road accidents.28

References1. J. Lee, Y.-W. Seo, and D. Wettergreen, Kernel-based tracking for improving sign detection performance, in Proc. IEEE/RSJ Int. Conf. IROS.nov 20132. L. D. Lopez and O. Fuentes, Color-based road sign detection and tracking,in Proc. ICIAR, 2007, pp. 11381147.29

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recognition of highway workzones for reliable autonomous driving

Engineering