The DOI matches its Crossref record; 158 of the 158 references it cites were found in a registry or an index.
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving
Open accessAbstract
Capturing uncertainty in object detection is indispensable for safe autonomous driving. In recent years, deep learning has become the de-facto approach for object detection, and many probabilistic object detectors have been proposed. However, there is no summary on uncertainty estimation in deep object detection, and existing methods are either built with different network architectures and uncertainty estimation methods, or evaluated on different datasets with a wide range of evaluation metrics. As a result, a comparison among methods remains challenging, as does the selection of a model that best suits a particular application. This paper aims to alleviate this problem by providing a review and comparative study on existing probabilistic object detection methods for autonomous driving applications. First, we provide an overview of practical uncertainty estimation methods in deep learning, and then systematically survey existing methods and evaluation metrics for probabilistic object detection. Next, we present a strict comparative study for probabilistic object detection based on an image detector and three public autonomous driving datasets. Finally, we present a discussion of the remaining challenges and future works. Code has been made available athttps://github.com/asharakeh/pod_compare.git.
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I Find Your Lack of Uncertainty in Computer Vision Disturbing
Valdenegro-Toro, M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00139 · Cited by 15
Introspective Perception for Long-Term Aerial Telemanipulation With Virtual Reality
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Metadata
- Authors
- Feng, D., Harakeh, A., Waslander, S. L., & Dietmayer, K.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2022-08
- Volume
- 23
- Issue
- 8
- Pages
- 9961-9980
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W3109915642
- Cited by
- 316
- References
- 158
- FWCI
- 17.81
- Type
- journal article
- Language
- en
- Open access
- green
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