Multi-modal sensor fusion-based deep neural network for end-to-end autonomous driving with scene understanding
This study aims to improve the performance and generalization capability of end-to-end autonomous driving with scene understanding leveraging deep learning and multimodal sensor fusion techniques. The designed end-to-end deep neural network takes as input the visual image and associated depth inf...
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Main Authors: | , , , |
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Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2022
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/159714 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | This study aims to improve the performance and generalization capability of
end-to-end autonomous driving with scene understanding leveraging deep learning
and multimodal sensor fusion techniques. The designed end-to-end deep neural
network takes as input the visual image and associated depth information in an
early fusion level and outputs the pixel-wise semantic segmentation as scene
understanding and vehicle control commands concurrently. The end-to-end deep
learning-based autonomous driving model is tested in high-fidelity simulated
urban driving conditions and compared with the benchmark of CoRL2017 and
NoCrash. The testing results show that the proposed approach is of better
performance and generalization ability, achieving a 100% success rate in static
navigation tasks in both training and unobserved situations, as well as better
success rates in other tasks than the prior models. A further ablation study
shows that the model with the removal of multimodal sensor fusion or scene
understanding pales in the new environment because of the false perception. The
results verify that the performance of our model is improved by the synergy of
multimodal sensor fusion with scene understanding subtask, demonstrating the
feasibility and effectiveness of the developed deep neural network with
multimodal sensor fusion. |
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