ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
THE RESEARCH ATLAS
EMMA: End-to-End Multimodal Model for Autonomous Driving
Builds on Gemini to predict driving trajectories, perception objects and road structure from camera inputs and text.
Institution and location sources
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Why read this
Observe how general multimodal knowledge transfers to driving rather than treating language models and autonomy as unrelated tracks.
What it connects
Waymo explicitly identifies Gemini as the foundation; multiple driving tasks share a language representation.
Research benchmark evaluation; not deployment proof
Task evaluations do not establish deployment as the commercial Waymo Driver. The paper identifies video-context, 3D sensor-fusion and compute limitations.
Follow these connections ↗Sources for relations and versions
Citation
@misc{hwang2024emma,
title = {EMMA: End-to-End Multimodal Model for Autonomous Driving},
author = {Jyh-Jing Hwang and Runsheng Xu and Hubert Lin and Wei-Chih Hung and Jingwei Ji and Kristy Choi and Di Huang and Tong He and Paul Covington and Benjamin Sapp and Yin Zhou and James Guo and Dragomir Anguelov and Mingxing Tan},
year = {2024},
eprint = {2410.23262},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2410.23262}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
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