The Digital Foundation Platform -A Multi-Layered SOA Architecture for Intelligent Connected Vehicle Operating System

Abstract

Legacy AD/ADAS development from OEMs centers around developing functions on ECUs using services provided by AUTOSAR Classic Platform (CP) to meet automotive-grade and mass-production requirements. The AUTOSAR CP couples hardware and software components statically and encounters challenges to provide sufficient capacities for the processing of high-level intelligent driving functions, whereas the new platform, AUTOSAR Adaptive Platform (AP) is designed to support dynamically communication and provide richer services and function abstractions for those resource-intensive (memory, CPU) applications.Yet for both platforms, application development and the supporting system software are still closely coupled together, and this makes application development and the enhancement less scalable and flexible, resulting in longer development cycles and slower time-tomarket.This paper presents a multi-layered, service-oriented intelligent driving operating system foundation (we named it as Digital Foundation Platform) that provides abstractions for easier adoption of heterogeneous computing hardware. It features a multi-layer SOA software architecture with each layer providing adaptive service API at north-bound for application developers.The proposed Digital Foundation Platform (DFP) has significant advantages of decoupling hardware, operating system core, middleware, functional software and application software development. It provides SOA at multiple layers and enables application developers from OEMs, to customize and develop new applications or enhance existing applications with new features, either in autonomous domain or intelligent cockpit domain, with great agility, and less code through re-usability, and thus reduce the time-to-market. network (NN), and computer vision are moving forward to allow higher levels of AD [4].With the four digital transformation trends-ACES, the complexity of software built into vehicles is growing exponentially. Add to this the massive explosion of sensor data, and the need to manage, and interpret all this data using AI techniques in real time and using communication technologies to transfer the data.

Review of Electrical and Electronic Architectures for Autonomous Vehicles: Topologies, Networking and Simulators

“…Haas et al [29], Baic et al [30], Saidi et al [31], Bandur et al [23], and Tavella et al [32] largely agreed with Navale et al, with the addition of the concept of zone orientation in the fifth stage. Yu et al [33] fully agreed with the roadmap of the development of E/E architecture topologies proposed by Navale et al in 2015. Zeng et al [13] reviewed the achievements and challenges of in-vehicle networks including LIN, CAN, FlexRay, Ethernet, and MOST, and further proposed future E/E architecture topologies with four switches and several domain masters.…”

“…Its usage enhances the flexibility of in-vehicle systems to adapt to operational changes [25,33,[68][69][70].…”