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
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
A 128$\times$128 120 dB 15 $\mu$s Latency Asynchronous Temporal Contrast Vision Sensor
Lichtsteiner, P., Posch, C., & Delbruck, T. (2008) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2007.914337 · Cited by 2,485
CNN Features off-the-shelf: an Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · arXiv · doi:10.48550/arxiv.1403.6382 · Cited by 607
Image-Based Localization Using LSTMs for Structured Feature Correlation
Walch, F., Hazirbas, C., Leal-Taixe, L., Sattler, T., Hilsenbeck, S., & Cremers, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.75 · Cited by 521
DDD17: End-To-End DAVIS Driving Dataset
Binas, J., Neil, D., Liu, S.-C., & Delbruck, T. (2017) · arXiv · doi:10.48550/arxiv.1711.01458 · Cited by 96
Research works printed but not linked: 19
- paul moeys, 2016, Steering a predator robot using a mixed frame/event-driven convolutional neural network, Int Conf Event-Based Control Comm Signal Proc (EBCCSP), 2
- buehler, 2009, The DARPA Urban Challenge Autonomous Vehicles in City Traffic, 56, 2
- pomerleau, 1989, ALVINN: An autonomous land vehicle in a neural network, Advances in Neural Information Processing Systems 1, 305
- bojarski, 2016, End to End Learning for Self-Driving Cars
- 10.1109/TPAMI.2013.71 · DOI · Paper page
- 10.1109/TPAMI.2015.2392947 · DOI · Paper page
- xavier, 2017, HOTS: A hierarchy of event-based time-surfaces for pattern recognition, IEEE Trans Pattern Anal Machine Intell, 39, 1346, 10.1109/TPAMI.2016.2574707 · DOI · Paper page
- iulia-alexandra, 2017, Live demonstration: Convolutional neural network driven by dynamic vision sensor playing RoShamBo, IEEE Int Symp Circuits Syst (ISCAS), 2
- guillermo, 2017, Elias Mueggler, Henri Rebecq, Tobi Delbruck, and Davide Scaramuzza. Event-based, 6-DOF camera tracking from photometric depth maps, IEEE Trans Pattern Anal Machine Intell, 1
- 10.1109/LRA.2016.2645143 · DOI · Paper page
- 10.1109/CVPR.2018.00407 · DOI · Paper page
- rebecq, 2017, EMVS: Event-based multi-view stereo-3D reconstruction with an event camera in real-time, Int J Comput Vis, 1
- 10.1109/ICCV.2017.320 · DOI · Paper page
- kim, 2016, Real-time 3D reconstruction and 6-DoF tracking with an event camera, Eur Conf Comput Vis (ECCV), 349
- huazhe, 2017, End-to-end learning of driving models from large-scale video datasets, IEEE Int Conf Comput Vis Pattern Recog (CVPR), 3530
- christian, 2014, A 240× 180 130dB 3us latency global shutter spatiotemporal vision sensor, IEEE J Solid-State Circuits, 49, 2333, 10.1109/JSSC.2014.2342715 · DOI · Paper page
- 10.1109/BioCAS.2013.6679698 · DOI
- 10.1109/CVPR.2016.319 · DOI · Paper page
- 10.1007/BF00992696 · DOI · Paper page
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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