A Survey on 3D Object Detection Methods for Autonomous Driving Applications

Arnold, Eduardo; Al-Jarrah, Omar Y.; Dianati, Mehrdad; Fallah, Saber; Oxtoby, David; Mouzakitis, Alexandros

doi:10.1109/tits.2019.2892405

Cited by 536 publications

(279 citation statements)

References 54 publications

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“…Traffic efficiency and reduced congestion can be obtained through the implementation of technological features such as communication from one vehicle to another (V2V) and between vehicles and roadside infrastructure (V2I) [31]. AVs can implement collaborative "perception" or data sharing about hazards or obstacles [32,33]. There is also the potential for AVs to safely drive more closely to each other than human-driven cars.…”

Section: Introductionmentioning

confidence: 99%

Driving Style: How Should an Automated Vehicle Behave?

et al. 2019

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This article reports on a study to investigate how the driving behaviour of autonomous vehicles influences trust and acceptance. Two different designs were presented to two groups of participants (n = 22/21), using actual autonomously driving vehicles. The first was a vehicle programmed to drive similarly to a human, “peeking” when approaching road junctions as if it was looking before proceeding. The second design had a vehicle programmed to convey the impression that it was communicating with other vehicles and infrastructure and “knew” if the junction was clear so could proceed without ever stopping or slowing down. Results showed non-significant differences in trust between the two vehicle behaviours. However, there were significant increases in trust scores overall for both designs as the trials progressed. Post-interaction interviews indicated that there were pros and cons for both driving styles, and participants suggested which aspects of the driving styles could be improved. This paper presents user information recommendations for the design and programming of driving systems for autonomous vehicles, with the aim of improving their users’ trust and acceptance.

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Section: Introductionmentioning

confidence: 99%

Driving Style: How Should an Automated Vehicle Behave?

et al. 2019

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“…The Convolutional Neural Network (CNN) is one of the most popular neural networks that have been well applied to many different research areas, such as image data augmentation [30], object detection [31] and text classification [32]. Features are the critical components for any machine learning model, especially for deep learning methods.…”

Section: B Cnn Modelmentioning

confidence: 99%

A Content-Based Method for Sybil Detection in Online Social Networks via Deep Learning

et al. 2020

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Online social networks (OSNs) are generally susceptible to Sybil attack, which causes a series of cybersecurity problems and privacy violations. Malicious attackers can create massive Sybils and further utilize those fake identities to launch various Sybil attacks. Therefore, Sybil detection in OSNs has become an urgent security research problem for both academia and industries. The existing content-based methods to detect Sybils base on the combination of manual-design features and machine learning algorithms, which requires lots of professional experiences and human effort. These methods divide the Sybil detection problem into two piece-wise sub-problems, which prevents us from getting the optimal solution. In this work, we propose a novel content-based method to detect Sybils. The proposed method is an end-to-end classification model that extracts features directly from the input data, and then output the classification results in a unified framework. The proposed method includes three main parts: first, the self-normalizing convolutional neural network (CNN) is adopted to extract lower features from the multi-dimensional input data; second, the bidirectional self-normalizing LSTM network (bi-SN-LSTM) is developed to extract higher features from the compressed feature map sequence; third, the dense layer and softmax classifier are stacked to output the classification results. Unlike the traditional bidirectional long short-term memory network (bi-LSTM), the proposed bi-SN-LSTM network utilizes SELU as the activation function of its recurrent step, which provides unbounded changes to the state value. Through the case study of the real-world dataset, the comparison experiments demonstrate that our method significantly outperforms other state-of-the-art methods. INDEX TERMS Convolutional neural network (CNN), deep learning (DL), long short-term memory (LSTM), online social networks (OSNs), sybil detection.

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“…Loss total = loss pg_cls + loss pg_reg + loss 3D_cls + loss 3D_reg + loss 3D_ang (4) The diversity of loss categories and computing methods make it difficult to optimize the network. In the experiments, the low regression accuracy in height dimension results in the AP 3D is more than 10% lower than the AP BEV .…”

Section: The Multi-task Loss Function and Loss Weight Masksmentioning

confidence: 99%

“…According to the sensors most commonly used in the traffic scene, 3D object detection methods can be roughly divided into three categories [4]: image-based, point-cloud-based, and fusion-based methods. Image data can provide abundant texture properties, which are of great significance to object recognition.…”

Section: Introductionmentioning

confidence: 99%

Feature Deep Continuous Aggregation for 3D Vehicle Detection

et al. 2019

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3D object detection has recently become a research hotspot in the field of autonomous driving. Although great progress has been made, it still needs to be further improved. Therefore, this paper presents FDCA, a feature deep continuous aggregation network using multi-sensors for 3D vehicle detection. The proposed network adopts a two-stage structure with the bird’s-eye view (BEV) map and the RGB image as an input. In the first stage, two feature extractors were used to generate feature maps with the high-resolution and representational ability for each input view. These feature maps were then fused and fed to a 3D proposal generator to obtain the reliable 3D vehicle proposals. In the second stage, the refinement network aggregated the features of the proposal regions further and performed classifications, a 3D bounding boxes regression, and orientation estimations to predict the location and heading of vehicles in 3D space. The FDCA network proposed was trained and evaluated on the KITTI 3D object detection benchmark. The experimental results of the validation set illustrated that compared with other fusion-based methods, the 3D average precision (AP) could achieve 76.82% on a moderate setting while having real-time capability, which was higher than that of the second-best performing method by 2.38%. Meanwhile, the results of ablation experiments show that the convergence rate of FDCA was much faster and the stability was also much better, making it a candidate for application in autonomous driving.

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A Survey on 3D Object Detection Methods for Autonomous Driving Applications

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 536 publications

References 54 publications

Driving Style: How Should an Automated Vehicle Behave?

Driving Style: How Should an Automated Vehicle Behave?

A Content-Based Method for Sybil Detection in Online Social Networks via Deep Learning

Feature Deep Continuous Aggregation for 3D Vehicle Detection

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