Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions

Deep Learning Methods Applied to 3D Point Clouds Based Instance Segmentation: A Review

10.20944/preprints202111.0228.v1 ◽

2021 ◽

Author(s):

Desire Mulindwa Burume ◽

Shengzhi Du

Keyword(s):

Deep Learning ◽

Semantic Segmentation ◽

Point Clouds ◽

Autonomous Driving ◽

Indoor Navigation ◽

Future Directions ◽

3D Point Clouds ◽

Speed Accuracy ◽

Learning Architectures ◽

Instance Segmentation

Beyond semantic segmentation,3D instance segmentation(a process to delineate objects of interest and also classifying the objects into a set of categories) is gaining more and more interest among researchers since numerous computer vision applications need accurate segmentation processes(autonomous driving, indoor navigation, and even virtual or augmented reality systems…) This paper gives an overview and a technical comparison of the existing deep learning architectures in handling unstructured Euclidean data for the rapidly developing 3D instance segmentation. First, the authors divide the 3D point clouds based instance segmentation techniques into two major categories which are proposal based methods and proposal free methods. Then, they also introduce and compare the most used datasets with regard to 3D instance segmentation. Furthermore, they compare and analyze these techniques performance (speed, accuracy, response to noise…). Finally, this paper provides a review of the possible future directions of deep learning for 3D sensor-based information and provides insight into the most promising areas for prospective research.

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Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2021.3071405 ◽

2021 ◽

pp. 1-1

Author(s):

Yao Deng ◽

Tiehua Zhang ◽

Guannan Lou ◽

Xi Zheng ◽

Jiong Jin ◽

...

Keyword(s):

Deep Learning ◽

Autonomous Driving ◽

Attacks And Defenses

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Deep Learning for Radio-based Human Sensing: Recent Advances and Future Directions

IEEE Communications Surveys & Tutorials ◽

10.1109/comst.2021.3058333 ◽

2021 ◽

pp. 1-1

Author(s):

Isura Nirmal ◽

Abdelwahed Khamis ◽

Mahbub Hassan ◽

Wen Hu ◽

Xiaoqing Zhu

Keyword(s):

Deep Learning ◽

Future Directions ◽

Recent Advances ◽

Human Sensing

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A Survey on Bias and Fairness in Machine Learning

ACM Computing Surveys ◽

10.1145/3457607 ◽

2021 ◽

Vol 54 (6) ◽

pp. 1-35

Author(s):

Ninareh Mehrabi ◽

Fred Morstatter ◽

Nripsuta Saxena ◽

Kristina Lerman ◽

Aram Galstyan

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Real World ◽

State Of The Art ◽

Future Directions ◽

Discriminatory Behavior ◽

Real World Applications ◽

Near Future ◽

Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

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On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data

Remote Sensing ◽

10.3390/rs13010089 ◽

2020 ◽

Vol 13 (1) ◽

pp. 89

Author(s):

Manuel Carranza-García ◽

Jesús Torres-Mateo ◽

Pedro Lara-Benítez ◽

Jorge García-Gutiérrez

Keyword(s):

Deep Learning ◽

Real Time ◽

Autonomous Vehicles ◽

Remote Sensing Data ◽

Autonomous Driving ◽

Two Stage ◽

Detection Systems ◽

One Stage ◽

Time Requirements ◽

Speed Accuracy

Object detection using remote sensing data is a key task of the perception systems of self-driving vehicles. While many generic deep learning architectures have been proposed for this problem, there is little guidance on their suitability when using them in a particular scenario such as autonomous driving. In this work, we aim to assess the performance of existing 2D detection systems on a multi-class problem (vehicles, pedestrians, and cyclists) with images obtained from the on-board camera sensors of a car. We evaluate several one-stage (RetinaNet, FCOS, and YOLOv3) and two-stage (Faster R-CNN) deep learning meta-architectures under different image resolutions and feature extractors (ResNet, ResNeXt, Res2Net, DarkNet, and MobileNet). These models are trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context. For the experimental study, we use the Waymo Open Dataset, which is the largest existing benchmark. Despite the rising popularity of one-stage detectors, our findings show that two-stage detectors still provide the most robust performance. Faster R-CNN models outperform one-stage detectors in accuracy, being also more reliable in the detection of minority classes. Faster R-CNN Res2Net-101 achieves the best speed/accuracy tradeoff but needs lower resolution images to reach real-time speed. Furthermore, the anchor-free FCOS detector is a slightly faster alternative to RetinaNet, with similar precision and lower memory usage.

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