This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Optimization-Based Robust Architecture Design for Autonomous Driving System
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 2, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
With the recent advancement in sensing and controller technologies architecture design of an autonomous driving system becomes an important issue. Researchers have been developing different sensors and data processing technologies to solve the issues associated with fast processing, diverse weather, reliability, long distance recognition performance, etc. Necessary considerations of diverse traffic situations and safety factors of autonomous driving have also increased the complexity of embedded software as well as architecture of autonomous driving. In these circumstances, there are almost countless numbers of possible architecture designs. However, these design considerations have significant impacts on cost, controllability, and system reliability. Thus, it is crucial for the designers to make a challenging and critical design decision under several uncertainties during the conceptual design phase. This paper proposes an optimization-based robust architecture design framework for an autonomous driving system. The proposed framework focuses mainly on two design processes. The first one deals with the hardware integration issue. In this process, processors and buses need to be selected from an available hardware list and connected to realize the hardware system. The second one addresses the issue of proper allocation of software to the integrated components. In this process, computational tasks are allocated to the selected processors. Similarly, message transmission between different processors is also assigned through the selected buses. These architecture design issues are formulated as an integer programming (IP) problem to manage them simultaneously. Since the architecture design involves multiple objectives, the design issues are solved as a multi-objective optimization problem in order to identify the set of compromise optimal solutions, which ultimately minimize hardware cost as well as end-to-end latency under constrains of feasibility and safety. Furthermore, the proposed framework is expanded to a robust design method against software uncertainty. As a result, the risk of becoming an infeasible design is reduced by 75%.
- Yuto Imanishi - Hitachi America, Ltd.
- Anne Collin - Massachusetts Institute of Technology
- Afreen Siddiqi - Massachusetts Institute of Technology
- Eric Rebentisch - Massachusetts Institute of Technology
- Taisetsu Tanimichi - Hitachi Automotive Systems Americas Inc.
- Yukti Matta - Hitachi Automotive Systems Americas Inc.
CitationImanishi, Y., Collin, A., Siddiqi, A., Rebentisch, E. et al., "Optimization-Based Robust Architecture Design for Autonomous Driving System," SAE Technical Paper 2019-01-0473, 2019, https://doi.org/10.4271/2019-01-0473.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
- Davare, A., Zhu, Q., Natale, D. M., Pinello, C. et al., “Period Optimization for Hard Real-Time Distributed Automotive Systems,” in 44th ACM/IEEE Design Automation Conference, 2007, doi:10.1109/DAC.2007.375172.
- Zheng, W., Zhu, Q., Natale, D. M., “Definition of Task Allocation and Priority Assignment in Hard Real-Time Distributed Systems,” in 28th IEEE International Real-Time Systems Symposium, 2007, doi:10.1109/RTSS.2007.40.
- Katoen, J.-P., Noll, T., Wu, H., Santen, T. et al., “Model-Based Energy Optimization of Automotive Control Systems,” in Design, Automation & Test in Europe Conference & Exhibition, 2013, doi:10.7873/DATE.2013.162.
- Yu, H., Joshi, J., Talpin, J.-P., Shukla, S., “The Challenge of Interoperability: Model-Based Integration for Automotive Control Software,” in 52nd ACM/EDAC/IEEE Design Automation Conference, 2015, doi:10.1145/2744769.2747945.
- Zheng, B., Liang, H., Zhu, Q., Yu, H. et al., “Next Generation Autonomous Architecture Modeling and Exploration for Autonomous Driving,” in IEEE Computer Society Annual Symposium on VLSI, 2016, doi:10.1109/ISVLSI.2016.126.
- Collin, A., Siddiqi, A., Imanishi, Y., Tanimichi, T. et al., “Autonomous Driving Systems Hardware and Software Architecture Exploration: Optimizing Latency and Cost under Safety Constraints,” presented at CESUN Global Conference, Tokyo, June 20-22, 2018.
- Schätz, B., Voss, S., and Zverlov, S., “Automating Design-Space Exploration: Optimal Development of Automotive SW-Components in an ISO26262 Context,” in 52nd ACM/EDAC/IEEE Design Automation Conference, 2015, doi:10.1145/2744769.2747912.
- Eberl, M., Glaß, M., Teich, J., and Abelein, U., “Considering Diagnosis Functionality during Automatic System-Level Design of Automotive Networks,” in DAC Design Automation Conference, 2012, doi:10.1145/2228360.2228400.
- Lin, C.-W., Zhu, Q., Phung, C., and Sangiovanni-Vincentelli, A., “Security-Aware Mapping for CAN-Based Real-Time Distributed Automotive Systems,” in IEEE/ACM International Conference on Computer-Aided Design, 2013, doi:10.1109/ICCAD.2013.6691106.
- Lin, C.-W., Zhu, Q., and Sangiovanni-Vincentelli, A., “Security-Aware Mapping for TDMA-Based Real-Time Distributed Systems,” in IEEE/ACM International Conference on Computer-Aided Design, 2014, doi:10.1109/ICCAD.2014.7001325.
- Abelein, U., Cook, A., Engelke, P., Glaß, M. et al., “Non-Intrusive Integration of Advanced Diagnosis Features in Automotive E/E-Architectures,” in Design, Automation & Test in Europe Conference & Exhibition, 2014, doi:10.7873/DATE.2014.373.
- Reimann, F., Glaß, M., Teich, J., Cook, A., “Advanced Diagnosis: SBST and BIST Integration in Automotive E/E Architectures,” in 51st ACM/EDAC/IEEE Design Automation Conference, 2014, doi:10.1145/2593069.2602971.
- Zheng, B., Deng, P., Anguluri, R., Zhu, Q. et al., “Cross-Layer Codesign for Secure Cyber-Physical Systems,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 35(5):699-711, 2016, doi:10.1109/TCAD.2016.2523937.
- Zheng, B., Lin, C.-W., Yu, H., Liang, H. et al., “CONVINCE: A Cross-Layer Modeling, Exploration and Validation Framework for Next-Generation Connected Vehicles,” in IEEE/ACM International Conference on Computer-Aided Design, 2016, doi:10.1145/2966986.2980078.
- Collin, A., Espinoza, T. A., “SLAM-Based Performance Quantification of Sensing Architectures for Autonomous Vehicles,” in International Conference on Vehicular Electronics and Safety, 2018.
- Davis, I.R., Burns, A., Bril, J.R., and Lukkien, J.J., “Controller Area Network (CAN) Schedulability Analysis: Refuted, Revisited and Revised,” Real-Time Systems 35(3):239-272, 2007, doi:10.1007/s11241-007-9012-7.
- Thiele, D., Ernst, R., “Formal Worst-Case Performance Analysis of Time-Sensitive Ethernet with Frame Preemption.” in IEEE 21st International Conference on Emerging Technologies and Factory Automation, 2016, doi:10.1109/ETFA.2016.7733740.