A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Ni, Jianjun; Chen, Yinan; Chen, Yan; Zhu, Jinxiu; Ali, Deena; Cao, Weidong

doi:10.3390/app10082749

Cited by 118 publications

(60 citation statements)

References 93 publications

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“…Other very recent surveys about deep learning methods and applications are available in the literature still in the self-driving cars domain [ 16 , 17 ]. However, the work by Kuutti et al [ 16 ] is mainly concerned with vehicle control rather than perception, whereas the one by Ni et al [ 17 ], similarly to [ 15 ], presents a wide review on the deep learning-based solutions used in self-driving urban vehicles tasks, such as object detection, lane recognition, and path planning. Another work still focused on deep learning-based control techniques, both for manipulation and navigation, is the one by Tai et al [ 18 ].…”

Section: Related Work and Survey Boundariesmentioning

confidence: 99%

Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review

Guastella

Muscato

2020

Sensors

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The problem of autonomous navigation of a ground vehicle in unstructured environments is both challenging and crucial for the deployment of this type of vehicle in real-world applications. Several well-established communities in robotics research deal with these scenarios such as search and rescue robotics, planetary exploration, and agricultural robotics. Perception plays a crucial role in this context, since it provides the necessary information to make the vehicle aware of its own status and its surrounding environment. We present a review on the recent contributions in the robotics literature adopting learning-based methods to solve the problem of environment perception and interpretation with the final aim of the autonomous context-aware navigation of ground vehicles in unstructured environments. To the best of our knowledge, this is the first work providing such a review in this context.

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Section: Related Work and Survey Boundariesmentioning

confidence: 99%

Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review

Guastella

Muscato

2020

Sensors

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Section: Introductionmentioning

confidence: 99%

“…The target detection algorithm based on deep learning has the advantages of high detection accuracy and strong robustness. It is widely used in environmental monitoring [7], autonomous driving [8], UAV scene analysis [9] and other scenarios. However, due to the low quality of underwater imaging, complex underwater environment, the different sizes or shapes and overlapping or occlusion of underwater organisms, the general target detection algorithm based on deep learning does not have a good detection effect on underwater organisms.…”

Section: Introductionmentioning

confidence: 99%

Underwater Biological Detection Algorithm Based on Improved Faster-RCNN

Shi

et al. 2021

Water

Self Cite

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Underwater organisms are an important part of the underwater ecological environment. More and more attention has been paid to the perception of underwater ecological environment by intelligent means, such as machine vision. However, many objective reasons affect the accuracy of underwater biological detection, such as the low-quality image, different sizes or shapes, and overlapping or occlusion of underwater organisms. Therefore, this paper proposes an underwater biological detection algorithm based on improved Faster-RCNN. Firstly, the ResNet is used as the backbone feature extraction network of Faster-RCNN. Then, BiFPN (Bidirectional Feature Pyramid Network) is used to build a ResNet–BiFPN structure which can improve the capability of feature extraction and multi-scale feature fusion. Additionally, EIoU (Effective IoU) is used to replace IoU to reduce the proportion of redundant bounding boxes in the training data. Moreover, K-means++ clustering is used to generate more suitable anchor boxes to improve detection accuracy. Finally, the experimental results show that the detection accuracy of underwater biological detection algorithm based on improved Faster-RCNN on URPC2018 dataset is improved to 88.94%, which is 8.26% higher than Faster-RCNN. The results fully prove the effectiveness of the proposed algorithm.

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“…A core self-driving technology can be broadly classified into four categorized: (i) sensing, (ii) perception, (iii) planning, and (iv) control [18][19][20][21]. Sensing refers to the capability of the autonomous vehicle to collect real-time data from a variety of sensors (i.e., cameras, ultrasonic sonars, LiDARs, and radars) connected with self-driving cars to extract useful information from the environment.…”

Section: Introductionmentioning

confidence: 99%

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

Rasib

Butt

Khalid

et al. 2021

Mathematical Problems in Engineering

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In the last couple of years, academia-industry collaboration has demonstrated rapid advancements in the development of self-driving vehicles. Since it is evident that self-driving vehicles are going to reshape the traditional transportation systems in near future through enhancement in safe and smart mobility, motion control in self-driving vehicles while performing driving tasks in a dynamic road environment is still a challenging task. In this regard, we present a comprehensive study considering the evolution of steering control methods for self-driving vehicles. Initially, we discussed an insight into the traditional steering systems of the vehicles. To the best of our knowledge, currently, there is no taxonomy available, which elaborates steering control methods for self-driving vehicles. In this paper, we present a novel taxonomy including different steering control methods which are categorized into deterministic and heuristic steering control methods. Concurrently, the abovementioned techniques are critically reviewed elaborating their strengths and limitations. Based on the analysis, key challenges/research gaps in existing steering control methods along with the possible solutions have been briefly discussed to improve the effectiveness of the steering system of self-driving vehicles.

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A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Cited by 118 publications

References 93 publications

Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review

Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review

Underwater Biological Detection Algorithm Based on Improved Faster-RCNN

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

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