用联合嵌入架构构建安全驾驶世界模型，减少数据依赖并提升规划稳定性。
HanoiWorld : A Joint Embedding Predictive Architecture BasedWorld Model for Autonomous Vehicle Controller
- 基于JEPA与RNN构建可长期规划的世界模型
- 在高速环境上实现低碰撞率，优于当前最先进方法
- 适合需要高安全性的自动驾驶控制器研发
当前强化学习在自动驾驶控制器中的应用存在数据需求量大、性能不足、不稳定且难以捕捉安全概念的问题，常因像素重建的特性而过度关注噪声特征。本文提出HanoiWorld，一种基于联合嵌入预测架构（JEPA）的世界模型，结合循环神经网络（RNN）实现长时横向规划，并具备高效推理能力。在Highway-Env不同环境下的实验表明，该模型能有效生成安全驾驶计划，相比现有顶尖基线方法，碰撞率显著降低。
原文摘要 · Abstract (English)
Current attempts of Reinforcement Learning for Autonomous Controller are data-demanding while the results are under-performed, unstable, and unable to grasp and anchor on the concept of safety, and over-concentrating on noise features due to the nature of pixel reconstruction. While current Self-Supervised Learningapproachs that learning on high-dimensional representations by leveraging the JointEmbedding Predictive Architecture (JEPA) are interesting and an effective alternative, as the idea mimics the natural ability of the human brain in acquiring new skill usingimagination and minimal samples of observations. This study introduces Hanoi-World, a JEPA-based world model that using recurrent neural network (RNN) formaking longterm horizontal planning with effective inference time. Experimentsconducted on the Highway-Env package with difference enviroment showcase the effective capability of making a driving plan while safety-awareness, with considerablecollision rate in comparison with SOTA baselines
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