Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 21, Issue 11, Pages 4670-4679Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2019.2943777
Keywords
Multi-task network; deep learning; traffic object detection; drivable area detection; lane line detection
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Funding
- National Natural Science Foundation of China [U1764264/61873165]
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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.
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