A Perception Augmentation System for Autonomous Vehicles. Kauten, C., Gupta, A., Bevly, D., Qin, X., Li, H., & Jenkins, A. In December, 2018.
Paper abstract bibtex We describe a system prototype for perception augmentation in autonomous vehicles. The system is built using a fully convolutional deep encoder-decoder architecture to map pixels with depth measures to semantic class labels. Class labels recombine with depth measures to produce a 3-dimensional semantic map of the objects in front of the vehicle. The map, simplified to highlight areas of importance (e.g., other vehicles, pedestrians), is shown to the passenger using a novel user interface. The map is also analyzed for potential risks to queue alerts to the passenger. Alerts are both: (1) shown to the passenger using an addressable LED strip around the windshield, and (2) delivered to the passenger through a speaker.
@inproceedings{kauten_perception_2018,
title = {A {Perception} {Augmentation} {System} for {Autonomous} {Vehicles}},
url = {https://aisel.aisnet.org/sigdsa2018/4/},
abstract = {We describe a system prototype for perception augmentation in autonomous vehicles. The system is built using a fully convolutional deep encoder-decoder architecture to map pixels with depth measures to semantic class labels. Class labels recombine with depth measures to produce a 3-dimensional semantic map of the objects in front of the vehicle. The map, simplified to highlight areas of importance (e.g., other vehicles, pedestrians), is shown to the passenger using a novel user interface. The map is also analyzed for potential risks to queue alerts to the passenger. Alerts are both:
(1) shown to the passenger using an addressable LED strip around the windshield, and (2) delivered to the passenger through a speaker.},
author = {Kauten, Christian and Gupta, Ashish and Bevly, David and Qin, Xiao and Li, Han and Jenkins, Alison},
month = dec,
year = {2018},
}
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