The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars
Open accessAbstract
Event cameras are bio-inspired vision sensors that naturally capture the dynamics of a scene, filtering out redundant information. This paper presents a deep neural network approach that unlocks the potential of event cameras on a challenging motion-estimation task: prediction of a vehicle's steering angle. To make the best out of this sensor-algorithm combination, we adapt state-of-the-art convolutional architectures to the output of event sensors and extensively evaluate the performance of our approach on a publicly available large scale event-camera dataset (~1000 km). We present qualitative and quantitative explanations of why event cameras allow robust steering prediction even in cases where traditional cameras fail, e.g. challenging illumination conditions and fast motion. Finally, we demonstrate the advantages of leveraging transfer learning from traditional to event-based vision, and show that our approach outperforms state-of-the-art algorithms based on standard cameras.
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Cites
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Cited by
Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3008413 · Cited by 2,566
Self-citationA Survey of Autonomous Driving: Common Practices and Emerging Technologies
Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889
“types gaining interest for ADS include flash cameras [77], thermal cameras [79], [80], and event cameras [78].”
BackgroundSelf-Balancing Federated Learning With Global Imbalanced Data in Mobile Systems
Duan, M., Liu, D., Chen, X., Liu, R., Tan, Y., & Liang, L. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.3009406 · Cited by 432
Events-To-Video: Bringing Modern Computer Vision to Event Cameras
Rebecq, H., Ranftl, R., Koltun, V., & Scaramuzza, D. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00398 · Cited by 420
Self-citationGallego, G., Rebecq, H., & Scaramuzza, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00407 · Cited by 341
Self-citation
Metadata
- Authors
- Maqueda, A. I., Loquercio, A., Gallego, G., Garcia, N., & Scaramuzza, D.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 5419-5427
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2796402180
- Cited by
- 514
- References
- 6
- FWCI
- 40.92
- Type
- conference paper
- Language
- en
- Open access
- green
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