HanoiWorld : A Joint Embedding Predictive Architecture BasedWorld Model for Autonomous Vehicle Controller

📝 Original Info

• Title: HanoiWorld : A Joint Embedding Predictive Architecture BasedWorld Model for Autonomous Vehicle Controller
• ArXiv ID: 2601.01577
• Date: 2026-01-04
• Authors: Tran Tien Dat, Nguyen Hai An, Nguyen Khanh Viet Dung, Nguyen Duy Duc

📝 Abstract

Current attempts of Reinforcement Learning for Autonomous Controller are data-demanding while the result are under-performed, unstable and unable to grapple and anchoring on the concept of safety, and over-concentrate on noise feature dues to the nature of pixel reconstruction. While current Self-Supervised Learning approachs that learning on high-dimensional representation by leveraging the Joint Embedding Predictive Architecture (JEPA) is interesting and effective alternative, as the idea is mimicking the natural of human's brain in acquiring new skill using imagination and minimal sample of observations. This study introduces Hanoi-World, a JEPA-based world model that using recurrent neural network (RNN) for making longterm horizontal planning with effective inference time. Experiments conducted on Highway-Env package with difference enviroment showcase the effective capability of making driving plan while safety-awareness with considerable collision rate in comparison with SOTA baselines.

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