As automotive electronic architectures grow increasingly complex and software development costs escalate, AUTOSAR plays a critical role in standardizing and enhancing the reusability of automotive controllers. However, existing AUTOSAR application layer modeling tools, such as the Simulink AUTOSAR Blockset, primarily adopt causal modeling paradigms, which constrain flexibility in capturing intricate system interactions. Additionally, their proprietary nature limits model accessibility, hindering cross-platform collaboration and multi-domain integration. Modelica, an object-oriented, equation-based modeling language, is particularly well-suited for multi-domain simulations due to its acausal modeling capabilities and strong support for component reuse. This paper proposes a Modelica-based visual modeling approach for AUTOSAR application layer models. Specifically, it establishes encapsulation rule for representing AUTOSAR constructs in Modelica and develops an open-source AUTOSAR model library, facilitating industry collaboration and accelerating rapid prototyping. A formal mathematical representation of AUTOSAR models is introduced to enhance both expressiveness and verifiability. Furthermore, a structured visual modeling methodology is presented to lower the development barrier for AUTOSAR application layer modeling. Comparative analysis with Simulink’s AUTOSAR Blockset demonstrates that the proposed approach successfully integrates Modelica’s multi-domain modeling capabilities into the AUTOSAR workflow while ensuring simulation consistency with Simulink. To the best of our knowledge, this work represents the first integration of AUTOSAR within Modelica’s multi-domain simulation framework. Compared to Simulink, Modelica’s acausal modeling paradigm enables more flexible system representations, its open ecosystem supports cross-platform collaboration, and its multi-domain integration enhances interoperability. Beyond the automotive domain, the proposed approach can also be applied to controller design in other industries, highlighting its potential for cross-disciplinary applications.