NeurIPS 2025

Embodied Cognition Augmented End2End
Autonomous Driving
Ling Niu
Tsinghua University
nl23@mails.tsinghua.edu.cn
Xiaoji Zheng
Tsinghua University
zhengxj24@mails.tsinghua.edu.cn
Han Wang
Tsinghua University
kelsulhgod@gmail.com
Chen Zheng
Tsinghua University
zhengchen@air.tsinghua.edu.cn
Ziyuan Yang
Tsinghua University
ziyuan86@uw.edu
Bokui Chen*
Tsinghua University
chenbokui@tsinghua.edu.cn
Jiangtao Gong∗
Tsinghua University
gongjiangtao2@gmail.com
Abstract
In recent years, vision-based end-to-end autonomous driving has emerged as a new
paradigm. However, popular end-to-end approaches typically rely on visual feature
extraction networks trained under label supervision. This limited supervision frame-
work restricts the generality and applicability of driving models. In this paper, we
propose a novel paradigm termed E3AD, which advocates for comparative learn-
ing between visual feature extraction networks and the general EEG large model,
in order to learn latent human driving cognition for enhancing end-to-end planning.
In this work, we collected a cognitive dataset for the mentioned contrastive learning
process. Subsequently, we investigated the methods and potential mechanisms
for enhancing end-to-end planning with human driving cognition, using popular
driving models as baselines on publicly available autonomous driving datasets.
Both open-loop and closed-loop tests are conducted for a comprehensive evalua-
tion of planning performance. Experimental results demonstrate that the E3AD
paradigm significantly enhances the end-to-end planning performance of baseline
models. Ablation studies further validate the contribution of driving cognition and
the effectiveness of comparative learning process. To the best of our knowledge,
this is the first work to integrate human driving cognition for improving end-to-end
autonomous driving planning. It represents an initial attempt to incorporate embod-
ied cognitive data into end-to-end autonomous driving, providing valuable i...