Optimization-Based Robust Architecture Design for Autonomous Driving System

Imanishi, Yuto; Collin, Anne; Siddiqi, Afreen; Rebentisch, Eric; Tanimichi, Taisetsu; Matta, Yukti

doi:10.4271/2019-01-0473

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Ref. [111] proposes a new formulation for architecture optimization, simultaneously exploring the design space of processor/ bus selection, physical architecture integration (including bus connections), and task/message assignment under uncertainty about the software processing load. Additionally, recent research has delved into the application of networking technologies, such as TSN [76][77][78][79][80][81][82][83][89][90][91][92][93][94][95], SDN [84,85,[89][90][91][92][93], SOA [86,94,95], aiming to enhance real-time capabilities, diagnostic and service functionalities, as well as safety and security within E/E architecture.…”

Section: Cost and Performance Optimizationmentioning

confidence: 99%

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

Wang,

Guo,

Cao

et al. 2024

Automot. Innov.

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With the rapid development of autonomous vehicles, more and more functions and computing requirements have led to the continuous centralization in the topology of electrical and electronic (E/E) architectures. While certain Tier1 suppliers, such as BOSCH, have previously proposed a serial roadmap for E/E architecture development, implemented since 2015 with significant contributions to the automotive industry, lingering misconceptions and queries persist in actual engineering processes. Notably, there are concerns regarding the perspective of zone-oriented E/E architectures, characterized by zonal concentration, as successors to domain-oriented E/E architectures, known for functional concentration. Addressing these misconceptions and queries, this study introduces a novel parallel roadmap for E/E architecture development, concurrently evaluating domain-oriented and zone-oriented schemes. Furthermore, the study explores hybrid E/E architectures, amalgamating features from both paradigms. To align with the evolution of E/E architectures, networking technologies must adapt correspondingly. The networking mechanisms pivotal in E/E architecture design are comprehensively discussed. Additionally, the study delves into modeling and verification tools pertinent to E/E architecture topologies. In conclusion, the paper outlines existing challenges and unresolved queries in this domain.

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Section: Cost and Performance Optimizationmentioning

confidence: 99%

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

Wang,

Guo,

Cao

et al. 2024

Automot. Innov.

View full text Add to dashboard Cite

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A Multiobjective Systems Architecture Model for Sensor Selection in Autonomous Vehicle Navigation

Collin

Siddiqi

Imanishi

et al. 2019

Complex Systems Design &Amp; Management

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Optimizing Architecture Transitions Using Decision Networks

Siddiqi

Rebentisch

Dorchuck

et al. 2020

Journal of Mechanical Design

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Architecture selection for systems undergoing rapid technological and market change is challenging. It is desirable to select architectures that can provide cost-effective possibilities for future changes and avoid architecture lock-in. However, optimal architectures for prevailing conditions may not be changeable for future adaptation. This tension between objectives for system (product) development for both short-term and long-term competitiveness has been an enduring challenge for system architects. Here, we use time-expanded decision networks with time varying costs and demands to systematically explore future architecture transition pathways and strategically identify useful designs. We demonstrate a new application for autonomous driving systems, a nascent technology, where the design and capabilities of constituent components (such as sensors, processors, and data communication links) are still evolving and significant market and regulatory uncertainties persist. In this case, we model technology costs with time-based factors to explicitly include future trends. The results show that as cost differences between architectures increases and demand for new functionality changes with time, the approach is able to identify potential transition points between architecture choices that optimize the net present value (NPV) of the system. For some of the specific scenarios analyzed in this study, the NPV with optimal architecture transitions is at least 10-20% larger as compared to fixed cases. Overall, this work presents a case for planning and partly constructing architecture transition roadmaps for new systems wherein dominant architectures have not emerged.

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Optimization-Based Robust Architecture Design for Autonomous Driving System

Cited by 3 publications

References 16 publications

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

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

A Multiobjective Systems Architecture Model for Sensor Selection in Autonomous Vehicle Navigation

Optimizing Architecture Transitions Using Decision Networks

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