This paper challenges a prevailing epistemological assumption in End-to-End Autonomous Driving: that high-performance planning necessitates high-fidelity world reconstruction. Inspired by cognitive science, we propose the Mental Bayesian Causal World Model (MBCWM) and instantiate it as the Tokenized Intent World Model (TIWM), a novel cognitive computing architecture. Its core philosophy posits that intelligence emerges not from pixel-level objective fidelity, but from the Cognitive Consistency between the agent's internal intentional world and physical reality. By synthesizing von Uexküll's $\textit{Umwelt}$ theory, the neural assembly hypothesis, and the triple causal model (integrating symbolic deduction, probabilistic induction, and force dynamics) into an end-to-end embodied planning system, we demonstrate the feasibility of this paradigm on the nuPlan benchmark. Experimental results in open-loop validation confirm that our Belief-Intent Co-Evolution mechanism effectively enhances planning performance. Crucially, in closed-loop simulations, the system exhibits emergent human-like cognitive behaviors, including map affordance understanding, free exploration, and self-recovery strategies. We identify Cognitive Consistency as the core learning mechanism: during long-term training, belief (state understanding) and intent (future prediction) spontaneously form a self-organizing equilibrium through implicit computational replay, achieving semantic alignment between internal representations and physical world affordances. TIWM offers a neuro-symbolic, cognition-first alternative to reconstruction-based planners, establishing a new direction: planning as active understanding, not passive reaction.

翻译：本文挑战了端到端自动驾驶领域一个盛行的认识论假设：高性能规划必须依赖高保真的世界重建。受认知科学启发，我们提出了心智贝叶斯因果世界模型（MBCWM），并将其实例化为一种新颖的认知计算架构——Tokenized Intent World Model（TIWM）。其核心哲学观点认为，智能并非源于像素级的客观保真度，而是产生于智能体内部意图世界与物理现实之间的认知一致性。通过将冯·于克斯屈尔的感知世界理论、神经集群假说以及三重因果模型（整合符号演绎、概率归纳与力动力学）综合成一个端到端的具身规划系统，我们在nuPlan基准测试中验证了该范式的可行性。开环验证的实验结果证实，我们的信念-意图协同演化机制能有效提升规划性能。尤为关键的是，在闭环仿真中，系统展现出类人的涌现认知行为，包括地图可供性理解、自由探索与自我恢复策略。我们将认知一致性确认为核心学习机制：在长期训练过程中，信念（状态理解）与意图（未来预测）通过隐式计算回放自发形成自组织平衡，实现了内部表征与物理世界可供性之间的语义对齐。TIWM为基于重建的规划器提供了一种神经符号、认知优先的替代方案，开创了新的研究方向：规划即主动理解，而非被动反应。