具身认知增强的端到端自动驾驶
Embodied Cognition Augmented End2End Autonomous Driving
AI总结：
本文提出名为 $E^{3}AD$ 的新范式，通过视觉特征提取网络与通用 EEG 大模型的对比学习来学习人类驾驶认知，从而显著增强端到端自动驾驶的规划性能。
摘要：
近年来，基于视觉的端到端自动驾驶已成为一种新范式。然而，流行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性和适用性。在本文中，我们提出了一种称为 $E^{3}AD$ 的新范式，该范式主张在视觉特征提取网络与通用 EEG 大模型之间进行对比学习，以学习潜在的人类驾驶认知，从而增强端到端规划。在这项工作中，我们为上述对比学习过程收集了一个认知数据集。随后，我们以流行的驾驶模型作为基线，在公开可用的自动驾驶数据集上，研究了利用人类驾驶认知增强端到端规划的方法和潜在机制。为了全面评估规划性能，我们进行了开环和闭环测试。实验结果表明，$E^{3}AD$ 范式显著增强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，这是首个整合人类驾驶认知来改进端到端自动驾驶规划的工作。它代表了将具身认知数据纳入端到端自动驾驶的初步尝试，为未来的类脑自动驾驶系统提供了有价值的见解。我们的代码将在 Github 上发布。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
