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.
THE RESEARCH ATLAS
Planning-oriented Autonomous Driving
Unifies perception, prediction and planning around the driving objective, using query interfaces to coordinate tasks.
Institution and location sources
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Why read this
Compare with EMMA: one starts from the driving task stack, the other from a general multimodal model. What does end-to-end learning mean in each?
What it connects
A starting point for multi-task representations and planning in specialized driving systems. Its connection to EMMA is an editorial comparison.
Conference paper and public benchmarks
First public in 2022, with a 2023 conference publication. Benchmark planning results do not directly establish open-road safety.
Follow these connections ↗Sources for relations and versions
Citation
@misc{hu2022planning,
title = {Planning-oriented Autonomous Driving},
author = {Yihan Hu and Jiazhi Yang and Li Chen and Keyu Li and Chonghao Sima and Xizhou Zhu and Siqi Chai and Senyao Du and Tianwei Lin and Wenhai Wang and Lewei Lu and Xiaosong Jia and Qiang Liu and Jifeng Dai and Yu Qiao and Hongyang Li},
year = {2022},
eprint = {2212.10156},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2212.10156}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
Related research
Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail
Connects driving reasoning and action prediction through Cosmos-Reason and a diffusion trajectory decoder, studying reasoning-action consistency.
GAIA-1: A Generative World Model for Autonomous Driving
Models video, text and vehicle actions as sequences to generate conditioned driving scenes and possible futures.