Self-supervised learning method for unstructured road detection using Fuzzy Support Vector Machines

• Road detection is a crucial problem in the application of autonomous vehicle and on-road mobile robot. Most of the recent methods only achieve reliable results in some particular well-arranged environments. In this paper, we describe a road detection algorithm for front-view monocular camera using road probabilistic distribution model (RPDM) and online learning method. The primary contribution of this paper is that the combination of dynamical RPDM and Fuzzy Support Vector Machines (FSVMs) makes the algorithm being capable of self-supervised learning and optimized learning from the inheritance of previous result. The secondary contribution of this paper is that the proposed algorithm uses road geometrical assumption to extract assumption based misclassified points and retrains itself online which makes it easier to find potential misclassified points. Those points take an important role in online retraining the classifier which makes the algorithm adaptive to environment changing.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Robotteknik och automation (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Robotics (hsv//eng)

Nyckelord

fuzzy set theory

geometry

learning (artificial intelligence)

mobile robots

object detection

probability

robot vision

support vector machines

autonomous vehicle

front-view monocular camera

fuzzy support vector machines

on-road mobile robot

online learning method

road probabilistic distribution model

self-supervised learning method

unstructured road detection algorithm

Publikations- och innehållstyp

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