A Protocol-Agnostic and Automated Approach to Bi-directional Inter-Domain Communication in Software-Defined Vehicles
Namcheol Lee et al.
Abstract
Software-defined vehicles (SDVs) are rapidly emerging as a technology enabler in the modern automotive industry, relying on centralized high-performance computing platforms where different vehicular domains are consolidated. This shift necessitates seamless inter-domain communication, especially between in-vehicle infotainment (IVI) and advanced driver assistance systems (ADAS) domains. However, enabling communication between such distinct domains presents challenges. The ADAS domain typically uses AUTOSAR, while the IVI domain uses Android, each with distinct communication protocols, interface definition languages (IDLs), and programming languages. Such heterogeneity creates complexities in protocol compatibility, IDL translation, and language binding. To address such problems, recent research has explored inter-domain communication. However, existing solutions are predominantly unidirectional, focusing on scenarios where IVI domain clients communicate with ADAS domain servers. In this paper, we propose an automated approach to bidirectional inter-domain communication, addressing the limitations of traditional approaches. Our approach is based on three key strategies: adoption of CommonAPI, automated IDL translation, and automated gateway implementation. We validate our approach through experiments on a multi-domain vehicle computer setup. Our approach significantly simplifies the development process, enhancing flexibility and reducing integration complexities in SDV development. This streamlines overall system development and maintenance and accelerates the adoption of SDV technology in the automotive industry.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.