Obstacle Detection and Safely Navigate the Autonomous Vehicle from Unexpected Obstacles on the Driving Lane.

Malik Haris,Jin Hou

doi:10.3390/s20174719

Abstract

Nowadays, autonomous vehicle is an active research area, especially after the emergence of machine vision tasks with deep learning. In such a visual navigation system for autonomous vehicle, the controller captures images and predicts information so that the autonomous vehicle can safely navigate. In this paper, we first introduced small and medium-sized obstacles that were intentionally or unintentionally left on the road, which can pose hazards for both autonomous and human driving situations. Then, we discuss Markov random field (MRF) model by fusing three potentials (gradient potential, curvature prior potential, and depth variance potential) to segment the obstacles and non-obstacles into the hazardous environment. Since the segment of obstacles is done by MRF model, we can predict the information to safely navigate the autonomous vehicle form hazardous environment on the roadway by DNN model. We found that our proposed method can segment the obstacles accuracy from the blended background road and improve the navigation skills of the autonomous vehicle.

Highlights

Global Status Report on Road Safety is released by World Health Organization (WHO, Geneva 27, Switzerland) 2018, in which WHO claims that about 1.35 million people die each year in road traffic accidents [1,2]
This study addresses how to improve the robustness of obstacle detection method in a complex environment, by integrating a Markov random field (MRF) for obstacles detection, road segmentation, and Convolutional Neural Network (CNN) model to navigate safely [20]
We can use the following quantitative indicators to analyze the performance of the model: root mean square error (RMSE) and mean absolute error (MAE), as well as qualitative indicators through

Summary

Introduction

Global Status Report on Road Safety is released by World Health Organization (WHO, Geneva 27, Switzerland) 2018, in which WHO claims that about 1.35 million people die each year in road traffic accidents [1,2]. American Automobile Association (AAA) Foundation released press report in 2016 that 50,658 vehicle roads accidents occurred only in America from the year 2011 to 2014 due to roadway obstacles. Roadway obstacles were the main factor of vehicle crashes and caused. Reports indicate that over 90% of crashes are caused by errors of driver [4]. To improve this situation, governments, municipal departments, and car manufacture companies have considered significant investments to support the development of various technologies such as autonomous vehicles and cognitive robots. About 1 billion euros already have been invested by EU agencies on such type of projects [5]

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Conclusion