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 framework restricts the generality and applicability of driving models. In this paper, we propose a novel paradigm termed $E^{3}AD$, which advocates for comparative learning 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 evaluation of planning performance. Experimental results demonstrate that the $E^{3}AD$ 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 embodied cognitive data into end-to-end autonomous driving, providing valuable insights for future brain-inspired autonomous driving systems. Our code will be made available at Github

翻译：近年来，基于视觉的端到端自动驾驶已成为一种新兴范式。然而，当前主流的端到端方法通常依赖于在标签监督下训练的视觉特征提取网络。这种有限的监督框架限制了驾驶模型的泛化能力与应用范围。本文提出一种名为$E^{3}AD$的新范式，主张通过视觉特征提取网络与通用EEG大模型之间的对比学习，以学习潜在的人类驾驶认知，从而增强端到端的规划能力。本研究首先构建了用于上述对比学习过程的认知数据集。随后，以公开自动驾驶数据集上的主流驾驶模型为基线，系统探究了利用人类驾驶认知增强端到端规划的方法及其潜在机制。通过开环与闭环测试对规划性能进行全面评估。实验结果表明，$E^{3}AD$范式显著提升了基线模型的端到端规划性能。消融研究进一步验证了驾驶认知的贡献以及对比学习过程的有效性。据我们所知，这是首次通过融合人类驾驶认知来改进端到端自动驾驶规划的研究。该工作是将具身认知数据引入端到端自动驾驶的初步尝试，为未来类脑自动驾驶系统提供了有价值的见解。相关代码将在Github平台开源。