Encoding Consistency: Optimizing Self-Driving Reliability With Real-Time Speed Data
Author:
Affiliation:
1. Tufts University, Medford, MA, United States of America
2. University of Missouri-Columbia, Columbia, MO, USA
3. Argonne National Laboratory, Lemont, IL, USA
Funder
National Science Foundation
NSF (National Science Foundation)
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3659994.3660308
Reference12 articles.
1. Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al. 2016. End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316 (2016).
2. Niklas Brusên. 2019. Simultaneous Localization and Mapping of a small-scale vehicle using low-cost IMU optical wheel encoders and LiDAR.
3. Waveshare Electronics. [n. d.]. PiRacer Pro AI Kit. https://www.waveshare.com/piracer-pro-ai-kit.htm. Accessed: 2024-4-12.
4. AutoLearn: Learning in the Edge to Cloud Continuum
5. Keahey Kate Anderson Jason Powers Mark and Cooper Adam. 2023. Three Pillars of Practical Reproducibility. In rewords23: 3rd Workshop on Reproducible Workflows Data Management and Security During eScience'23.
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