Overview of Environment Perception for Intelligent Vehicles

Zhu, Hao; Yuen, Ka‐Veng; Mihaylova, Lyudmila; Leung, Henry

doi:10.1109/tits.2017.2658662

Cited by 284 publications

(118 citation statements)

References 175 publications

(199 reference statements)

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“…can be found in Zhu, Yuen, Mihaylova, and Leung (2017) Figure 3. Being richer in information, image data are more suited for the object recognition task.…”

Section: Surveys Dedicated To Autonomous Vision and Environment Percementioning

confidence: 96%

A survey of deep learning techniques for autonomous driving

Grigorescu

Trasnea

Cocias

et al. 2019

Journal of Field Robotics

994

465

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The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. We investigate both the modular perception‐planning‐action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources, and computational hardware. The comparison presented in this survey helps gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices.

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“…can be found in Zhu, Yuen, Mihaylova, and Leung (2017) Figure 3. Being richer in information, image data are more suited for the object recognition task.…”

Section: Surveys Dedicated To Autonomous Vision and Environment Percementioning

confidence: 96%

A survey of deep learning techniques for autonomous driving

Grigorescu

Trasnea

Cocias

et al. 2019

Journal of Field Robotics

994

465

View full text Add to dashboard Cite

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“…is converted to a conventional mathematical model with only the path length as a fitness function [23]. In the formula 2 f !…”

Section: Particle Fitness Functionmentioning

confidence: 99%

Application of Multi-sensor Information Fusion Based on Improved Particle Swarm Optimization in Unmanned System Path Planning

Shi¹,

He²,

Li³

et al. 2017

Int. J. Onl. Eng.

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Abstract-Intelligent vehicle driving performance is safe and stable, which can significantly improve the efficiency of road traffic and reduce energy consumption, and intelligent vehicle is also the development direction of modern transport. Its core technology is intelligent environment perception module, by using a variety of sensors on the car in which the surrounding environment for data collection, processing module to provide effective control for the basis. In this paper, a new SINS / CNS / GPS integrated navigation observation equation is proposed, and a new federated data fusion structure is designed for the integrated navigation system. The particle filter is used to fuse the multi-source data of the federated filter subsystem, thus eliminating the limitations of the classical Kalman filter. The traditional Kalman filter structure and the federal particle filter mechanism are designed. The comparison shows that the proposed algorithm is effective in the information fusion of the integrated navigation system, and the filtering effect is superior to the traditional filtering method. Keywords-particle filter; information fusion; intelligent vehicle; navigation IntroductionWith the progress of society, the car has become an essential travel means of transport, vehicle congestion, traffic accidents and other issues are increasingly apparent. The rapid growth in the number of cars is caused by the low efficiency of public transport which leads to frequent traffic accidents. The establishment of a modern intelligent transportation system will be mentioned on the agenda. Intelligent Vehicles, as an important part of Intelligent Transportation Systems, is also the main body of the system, which not only improves driving safety and road traffic efficiency, but also reduces energy consumption [1][2][3]. Due to many advantages, the technology research has been increasingly concerned about the relevant institutions at home and abroad. Intelligent transportation system can effectively relieve traffic pressure, rational allocation of public transport resources and road resources. Based on machine sensing technology and control technology, the driving system uses information transmission technology and computer vision technology helps to monitor the road 88

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“…Hand-crafted image feature methods such as the histogram of oriented gradient (HOG; Dalal & Triggs, 2005) and scale-invariant feature transform (SIFT) have been widely applied (Zhu, Yuen, Mihaylova, & Leung, 2017). In order to classify images, image features are required.…”

Section: Introductionmentioning

confidence: 99%

“…In order to classify images, image features are required. Hand-crafted image feature methods such as the histogram of oriented gradient (HOG; Dalal & Triggs, 2005) and scale-invariant feature transform (SIFT) have been widely applied (Zhu, Yuen, Mihaylova, & Leung, 2017). However, state-of-the-art automatically learned features by convolutional neural networks (CNNs) have outperformed all the hand-crafted feature methods on large datasets (Krizhevsky, Sutskever, & Hinton, 2012).…”

Section: Introductionmentioning

confidence: 99%

Wildlife surveillance using deep learning methods

Chen

Little

Mihaylova

et al. 2019

Ecology and Evolution

Self Cite

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Wildlife conservation and the management of human–wildlife conflicts require cost‐effective methods of monitoring wild animal behavior. Still and video camera surveillance can generate enormous quantities of data, which is laborious and expensive to screen for the species of interest. In the present study, we describe a state‐of‐the‐art, deep learning approach for automatically identifying and isolating species‐specific activity from still images and video data. We used a dataset consisting of 8,368 images of wild and domestic animals in farm buildings, and we developed an approach firstly to distinguish badgers from other species (binary classification) and secondly to distinguish each of six animal species (multiclassification). We focused on binary classification of badgers first because such a tool would be relevant to efforts to manage Mycobacterium bovis (the cause of bovine tuberculosis) transmission between badgers and cattle. We used two deep learning frameworks for automatic image recognition. They achieved high accuracies, in the order of 98.05% for binary classification and 90.32% for multiclassification. Based on the deep learning framework, a detection process was also developed for identifying animals of interest in video footage, which to our knowledge is the first application for this purpose. The algorithms developed here have wide applications in wildlife monitoring where large quantities of visual data require screening for certain species.

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Overview of Environment Perception for Intelligent Vehicles

Cited by 284 publications

References 175 publications

A survey of deep learning techniques for autonomous driving

A survey of deep learning techniques for autonomous driving

Application of Multi-sensor Information Fusion Based on Improved Particle Swarm Optimization in Unmanned System Path Planning

Wildlife surveillance using deep learning methods

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