DLT-Net: Joint Detection of Drivable Areas, Lane Lines, and Traffic Objects
DLT-Net: Joint Detection of Drivable Areas, Lane Lines, and Traffic Objects
- New search for: Qian, Yeqiang
- Further information on Qian, Yeqiang:
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https://orcid.org/0000-0003-0831-8702
- New search for: Dolan, John M.
- Further information on Dolan, John M.:
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https://orcid.org/0000-0003-2062-100X
- New search for: Yang, Ming
- Further information on Yang, Ming:
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https://orcid.org/0000-0002-8679-9137
- New search for: Qian, Yeqiang
- Further information on Qian, Yeqiang:
-
https://orcid.org/0000-0003-0831-8702
- New search for: Dolan, John M.
- Further information on Dolan, John M.:
-
https://orcid.org/0000-0003-2062-100X
- New search for: Yang, Ming
- Further information on Yang, Ming:
-
https://orcid.org/0000-0002-8679-9137
Perception is an essential task for self-driving cars, but most perception tasks are usually handled independently. We propose a unified neural network named DLT-Net to detect drivable areas, lane lines, and traffic objects simultaneously. These three tasks are most important for autonomous driving, especially when a high-definition map and accurate localization are unavailable. Instead of separating tasks in the decoder, we construct context tensors between sub-task decoders to share designate influence among tasks. Therefore, each task can benefit from others during multi-task learning. Experiments show that our model outperforms the conventional multi-task network in terms of the task-wise accuracy and the overall computational efficiency, in the challenging BDD dataset.
Document information
DLT-Net: Joint Detection of Drivable Areas, Lane Lines, and Traffic Objects
IEEE Transactions on Intelligent Transportation Systems ; 21 , 11 ; 4670-4679
2020-11-01
2580323 byte
Article (Journal)
Electronic Resource
English
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