Road junction detection from 3D point clouds

Abstract

Detecting changing traffic conditions is of primal importance for the safety of autonomous cars navigating in urban environments. Among the traffic situations that require more attention and careful planning, road junctions are the most significant. This work presents an empirical study of the application of well known machine learning techniques to create a robust method for road junction detection. Features are extracted from 3D pointclouds corresponding to single frames of data collected by a laser rangefinder. Three well known classifiers-support vector machines, adaptive boosting and artificial neural networks-are used to classify them into "junctions" or "roads". The best performing classifier is used in the next stage, where structured classifiers-hidden Markov models and conditional random fields-are used to incorporate contextual information, in an attempt to improve the performance of the method. We tested and compared these approaches on datasets from two different 3D laser scanners, and in two different countries, Germany and Brazil.

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

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Traffic Intersection Re-Identification Using Monocular Camera Sensors

“…The depth cues of each pixel effectively provide more accurate intersection detection. Habermann et al [10] proposed to detect road intersections based on 3D point clouds. Three classifiers, including support vector machine (SVM), AdaBoost, and artificial neural network (ANN), are used for classification of intersections.…”

Navigation Application with Safety Features

“…In this paper [12], the main focus falls on how to detect the changing traffic conditions because they're of vital importance when it comes to the safety of autonomous cars that navigate urban environments. One of the most critical traffic problems that require a lot of attention is road junctions.…”