DeepRange: deep‐learning‐based object detection and ranging in autonomous driving

Parmar, Yashrajsinh; Natarajan, S. P.; Sobha, Gayathri

doi:10.1049/iet-its.2018.5144

Cited by 32 publications

(14 citation statements)

References 36 publications

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“…14, the improved Faster RCNN can detect both the parking car and lines, and the detection score is higher than the general Faster RCNN. The results in Table III show that the improved Faster RCNN has higher accuracy in all the situations than the other two methods, and the standard [11]. IV.…”

Section: Discussionmentioning

confidence: 87%

“…The specific tasks of environment perception in the field of self-driving include scene classification, obstacle detection, lane recognition, etc [9], [10]. Scene classification is one of the most important and challenging tasks in the self-driving car field, because the environment of the traffic is complicated and volatile, and the categories are various [11].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Deep Network-Based Scene Classification Method for Self-Driving Cars

Shen

Chen

et al. 2022

IEEE Trans. Instrum. Meas.

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Self-driving car is a hot research topic in the field of intelligent transportation system, which can greatly alleviate traffic jams and improve travel efficiency. Scene classification is one of the key technologies of self-driving cars, which can provide the basis for decision-making in self-driving cars. In recent years, deep learning-based solutions have achieved good results in the problem of scene classification. However, some problems should be further studied in the scene classification methods, such as how to deal with the similarities among different categories and the differences among the same category. To deal with these problems, an improved deep network-based scene classification method is proposed in this paper. In the proposed method, an improved Faster RCNN (Regions with CNN features) network is used to extract the features of representative objects in the scene to obtain local features, where a new residual attention block is added to the Faster RCNN network to highlight local semantics related to driving scenarios. In addition, an improved Inception module is used to extract global features, where a mixed Leaky ReLU and ELU function is presented, to reduce the possible redundancy of the convolution kernel and enhance the robustness. Then the local features and the global features are fused to realize the scene classification.Finally, a private data set is built from the public data sets for the specialized application of scene classification in the self-driving field and the proposed method is tested on the proposed data set. The experimental results show that the accuracy of the proposed method can reach 94.76%, which is higher than the state-of-the-art methods.

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Section: Discussionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

An Improved Deep Network-Based Scene Classification Method for Self-Driving Cars

Shen

Chen

et al. 2022

IEEE Trans. Instrum. Meas.

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show abstract

“…It can detect obstacles and identify obstacle-aware regions by implementing a deep encoder-decoder network. Parmar, et al [48] proposed an improved CNNs based on the addition of a range estimation layer, which can accomplish obstacle detection, classification and ranking simultaneously.…”

Section: Obstacle Detectionmentioning

confidence: 99%

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

Chen

et al. 2020

Applied Sciences

118

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Self-driving cars are a hot research topic in science and technology, which has a great influence on social and economic development. Deep learning is one of the current key areas in the field of artificial intelligence research. It has been widely applied in image processing, natural language understanding, and so on. In recent years, more and more deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. This paper presents a review of recent research on theories and applications of deep learning for self-driving cars. This survey provides a detailed explanation of the developments of self-driving cars and summarizes the applications of deep learning methods in the field of self-driving cars. Then the main problems in self-driving cars and their solutions based on deep learning methods are analyzed, such as obstacle detection, scene recognition, lane detection, navigation and path planning. In addition, the details of some representative approaches for self-driving cars using deep learning methods are summarized. Finally, the future challenges in the applications of deep learning for self-driving cars are given out.

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“…As a recently emerged concept in machine learning, deep learning has shown significant results in lots of areas such as image processing, object tracking [34], fault detection in bearing [35], and power systems [36,37]. In this regard, deep learning methods have shown great ability in understanding the long-term dependencies across time series and capturing detailed information from raw data.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning architecture for direct probability density prediction of small‐scale solar generation

Afrasiabi

Mohammadi

Rastegar

et al. 2020

IET Generation, Transmission & Distribution

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DeepRange: deep‐learning‐based object detection and ranging in autonomous driving

Cited by 32 publications

References 36 publications

An Improved Deep Network-Based Scene Classification Method for Self-Driving Cars

An Improved Deep Network-Based Scene Classification Method for Self-Driving Cars

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

Deep learning architecture for direct probability density prediction of small‐scale solar generation

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