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Abstract
Analysis
Behavior Analysis
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Civil Engineering

Overview of Environment Perception for Intelligent Vehicles

Profile image of Lyudmila MihaylovaLyudmila Mihaylova

IEEE Transactions on Intelligent Transportation Systems

https://doi.org/10.1109/TITS.2017.2658662
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19 pages

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Abstract

This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area. Index Terms-Intelligent vehicles, environment perception and modeling, lane and road detection, traffic sign recognition, vehicle tracking and behavior analysis, scene understanding. I. INTRODUCTION R ESEARCH and development on environmental perception, advanced sensing, and intelligent driver assistance systems aim at saving human lives. A wealth of research has been dedicated to the development of driver assistance systems and intelligent vehicles for safety enhancement [1], [2]. For the purposes of safety, comfortability, and saving energy, the field of intelligent vehicles has become a major research and development topic in the world. Many government agencies, academics, and industries invest great amount of resources on intelligent vehicles, such

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    A Review of Point Set Registration: From Pairwise Registration to Groupwise Registration
    Lyudmila S Mihaylova

    Sensors

    This paper presents a comprehensive literature review on point set registration. The state-of-the-art modeling methods and algorithms for point set registration are discussed and summarized. Special attention is paid to methods for pairwise registration and groupwise registration. Some of the most prominent representative methods are selected to conduct qualitative and quantitative experiments. From the experiments we have conducted on 2D and 3D data, CPD-GL pairwise registration algorithm and JRMPC groupwise registration algorithm seem to outperform their rivals both in accuracy and computational complexity. Furthermore, future research directions and avenues in the area are identified.

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    Robust Non-Rigid Feature Matching for Image Registration Using Geometry Preserving
    Lyudmila S Mihaylova

    Sensors

    In this paper, a robust non-rigid feature matching approach for image registration with geometry constraints is proposed. The non-rigid feature matching approach is formulated as a maximum likelihood (ML) estimation problem. The feature points of one image are represented by Gaussian mixture model (GMM) centroids, and are fitted to the feature points of the other image by moving coherently to encode the global structure. To preserve the local geometry of these feature points, two local structure descriptors of the connectivity matrix and Laplacian coordinate are constructed. The expectation maximization (EM) algorithm is applied to solve this ML problem. Experimental results demonstrate that the proposed approach has better performance than current state-of-the-art methods.

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    A Deep Learning Framework for Joint Image Restoration and Recognition
    Lyudmila Mihaylova

    Circuits, Systems, and Signal Processing

    Image restoration and recognition are important computer vision tasks representing an inherent part of autonomous systems. These two tasks are often implemented in a sequential manner, in which the restoration process is followed by a recognition. In contrast, this paper proposes a joint framework that simultaneously performs both tasks within a shared deep neural network architecture. This joint framework integrates the restoration and recognition tasks by incorporating: (i) common layers, (ii) restoration layers and (iii) classification layers. The total loss function combines the restoration and classification losses. The proposed joint framework, based on capsules, provides an efficient solution that can cope with challenges due to noise, image rotations and occlusions. The developed framework has been validated and evaluated on a public vehicle logo dataset under various degradation conditions, including Gaussian noise, rotation and occlusion. The results show that the joint framework improves the accuracy compared with the single task networks.

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    Road Infrastructure Challenges Faced by Automated Driving: A Review
    Cornelia Lex

    Applied Sciences

    Automated driving can no longer be referred to as hype or science fiction but rather a technology that has been gradually introduced to the market. The recent activities of regulatory bodies and the market penetration of automated driving systems (ADS) demonstrate that society is exhibiting increasing interest in this field and gradually accepting new methods of transport. Automated driving, however, does not depend solely on the advances of onboard sensor technology or artificial intelligence (AI). One of the essential factors in achieving trust and safety in automated driving is road infrastructure, which requires careful consideration. Historically, the development of road infrastructure has been guided by human perception, but today we are at a turning point at which this perspective is not sufficient. In this study, we review the limitations and advances made in the state of the art of automated driving technology with respect to road infrastructure in order to identify gaps th...

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    A Lightweight Network for Real-Time Rain Streaks and Rain Accumulation Removal from Single Images Captured by AVs
    Esraa Khatab

    Applied Sciences

    In autonomous driving, object detection is considered a base step to many subsequent processes. However, object detection is challenged by loss in visibility caused by rain. Rainfall occurs in two main forms, which are streaks and streaks accumulations. Each degradation type imposes different effect on the captured videos; therefore, they cannot be mitigated in the same way. We propose a lightweight network which mitigates both types of rain degradation in real-time, without negatively affecting the object-detection task. The proposed network consists of two different modules which are used progressively. The first one is a progressive ResNet for rain streaks removal, while the second one is a transmission-guided lightweight network for rain streak accumulation removal. The network has been tested on synthetic and real rainy datasets and has been compared with state-of-the-art (SOTA) networks. Additionally, time performance evaluation has been performed to ensure real-time performan...

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