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Volume: 30 | Article ID: art00019
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Understanding Vehicle E/E Architecture Topologies for Automated Driving: System Partitioning and Tradeoff Parameters
  DOI :  10.2352/ISSN.2470-1173.2018.17.AVM-358  Published OnlineJanuary 2018
Abstract

Highly Automated Driving is an active research and development area in automotive market for next generation series production. The development of automated functions like Highway driving or Parking gets partitioned across Edge and Central ECU's as part of vehicle E/E network. The paper introduces typical vehicle E/E topologies with emphasis on ADAS/AD domain with multiple intra/inters connectivity options. Within the ADAS/AD domain, various E/E system architecture topologies with multiple ECU partitioning are under exploration to optimize various parameters. The paper explains multiple topologies by analyzing two example system topologies. Topology-I enables incremental approach on the top of legacy ECUs, while topology-II enables cost optimized solution. The paper also explains functional partitioning of automated driving functionality (e.g. Highway driving, Parking) within ADAS/AD domain. This involves splitting of automated function in given topology across multiple ECUs in terms of perception (camera, radar & lidar), localization, fusion, driving policy, motion planning and control. The paper lists various parameters for considerations for given topologies e.g. Number of ECUs, intra domain connection bandwidth, functional safety, incremental development with legacy ECUs, cost and ease of software development. The paper ends with summarizing of these partitioning of functions and parameters tradeoffs enabling users to analyze custom E/E architectures.

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Mihir Mody, Jason Jones, Kedar Chitnis, Rajat Sagar, Gregory Shurtz, Yashwant Dutt, Manoj Koul, MG Biju, Aish Dubey, "Understanding Vehicle E/E Architecture Topologies for Automated Driving: System Partitioning and Tradeoff Parametersin Proc. IS&T Int’l. Symp. on Electronic Imaging: Autonomous Vehicles and Machines,  2018,  pp 358-1 - 358-5,  https://doi.org/10.2352/ISSN.2470-1173.2018.17.AVM-358

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