Interface-Centric Leadership Framework for ADAS–Restraint Applications Engineering and Vehicle Integration
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Abstract
The decisions that a vehicle makes regarding braking in case of a crash are now determined by advanced driver assistance functions, which are being fed by restraint systems. This connection fosters interfaces which are not owned by any single functionality team. Preexisting literature characterises sensors, functional safety, scenario testing, but these are discussed separately, task structure and team coordination. The paper suggests an interface centric leadership framework to ADAS restraint applications engineering and vehicle integration. There are four layers within the framework and they are interface identification, interface ownership, interface verification and interface governance. Two scoring functions rank interfaces by criticality and the ability of the leaders to cover the critical set. A timing margin equation is used to connect the decision of the leaders with the pre-crash window that is provided to the restraint controller. The experiment conducted by the study evaluated the framework using a program test with 42 interfaces in three releases of the vehicle program. The experiment was between a baseline team which was functional headed and a team which was interface headed. The interface led team discovered 71 percent of its integration defects prior to vehicle level test, as compared to 38 percent of the baseline. The number of late interface changes decreased from 19 to 6. The average time interval closes an interface defect dropped to 8.1 days as compared to 14.2 days. Pre-crash timing margin increased to 63 ms as compared to 41 ms. Findings indicate that leadership associated with interfaces enhances the quality of integrations as well as restraint preparedness. The model provides engineering managers with a direct approach to allocating responsibility and safeguarding schedule differences.
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References
[1] E. Marti, M. A. de Miguel, F. Garcia, and J. Perez, “A Review of Sensor Technologies for Perception in Automated Driving,” IEEE Intell. Transp. Syst. Mag., vol. 11, no. 4, pp. 94–108, 2019, doi: 10.1109/MITS.2019.2907630.
[2] V. K. Kukkala, J. Tunnell, S. Pasricha, and T. Bradley, “Advanced Driver-Assistance Systems: A Path Toward Autonomous Vehicles,” IEEE Consum. Electron. Mag., vol. 7, no. 5, pp. 18–25, 2018, doi: 10.1109/MCE.2018.2828440.
[3] E. Yurtsever, J. Lambert, A. Carballo, and K. Takeda, “A Survey of Autonomous Driving: Common Practices and Emerging Technologies,” IEEE Access, vol. 8, pp. 58443–58469, 2020, doi: 10.1109/ACCESS.2020.2983149.
[4] M. Unger, M. Tijssens, and J. Schüling, “Higher Occupant Safety with Active Safety Systems,” ATZ Worldw., vol. 118, no. 2, pp. 48–51, 2016, doi: 10.1007/s38311-015-0091-0.
[5] F. J. Belmonte, S. Martín, E. Sancristobal, J. A. Ruipérez-Valiente, and M. Castro, “Overview of Embedded Systems to Build Reliable and Safe ADAS and AD Systems,” IEEE Intell. Transp. Syst. Mag., vol. 13, no. 4, pp. 239–250, 2021, doi: 10.1109/MITS.2019.2953543.
[6] H.-K. Lee, S.-G. Shin, and D.-S. Kwon, “Design of emergency braking algorithm for pedestrian protection based on multi-sensor fusion,” Int. J. Automot. Technol., vol. 18, no. 6, pp. 1067–1076, 2017, doi: 10.1007/s12239-017-0104-7.
[7] T. S. Haque et al., “A Review on Driving Control Issues for Smart Electric Vehicles,” IEEE Access, vol. 9, pp. 135440–135472, 2021, doi: 10.1109/ACCESS.2021.3116353.
[8] S. Kugele, P. Obergfell, and E. Sax, “Model-based resource analysis and synthesis of service-oriented automotive software architectures,” Softw. Syst. Model., vol. 20, no. 6, pp. 1945–1975, 2021, doi: 10.1007/s10270-021-00896-9.
[9] M. Wagner, A. Meroth, and D. Zöbel, “Developing self-adaptive automotive systems,” Des. Autom. Embed. Syst., vol. 18, no. 3, pp. 199–221, 2014, doi: 10.1007/s10617-013-9124-3.
[10] G. Xie, Y. Li, Y. Han, Y. Xie, G. Zeng, and R. Li, “Recent Advances and Future Trends for Automotive Functional Safety Design Methodologies,” IEEE Trans. Ind. Informatics, vol. 16, no. 9, pp. 5629–5642, 2020, doi: 10.1109/TII.2020.2978889.
[11] S. Riedmaier, T. Ponn, D. Ludwig, B. Schick, and F. Diermeyer, “Survey on Scenario-Based Safety Assessment of Automated Vehicles,” IEEE Access, vol. 8, pp. 87456–87477, 2020, doi: 10.1109/ACCESS.2020.2993730.
[12] Z. Szalay, “Next Generation X-in-the-Loop Validation Methodology for Automated Vehicle Systems,” IEEE Access, vol. 9, pp. 35616–35632, 2021, doi: 10.1109/ACCESS.2021.3061732.
[13] S. D. Eppinger, D. E. Whitney, R. P. Smith, and D. A. Gebala, “A model-based method for organizing tasks in product development,” Res. Eng. Des., vol. 6, no. 1, pp. 1–13, 1994, doi: 10.1007/BF01588087.
[14] M. E. Sosa, “A structured approach to predicting and managing technical interactions in software development,” Res. Eng. Des., vol. 19, no. 1, pp. 47–70, 2008, doi: 10.1007/s00163-007-0039-5.
[15] M. Soltani and E. Knauss, “Challenges of Requirements Engineering in AUTOSAR ecosystems,” in 2015 IEEE 23rd International Requirements Engineering Conference (RE), 2015, pp. 294–295. doi: 10.1109/RE.2015.7320445.
[16] G. Liebel, M. Tichy, E. Knauss, O. Ljungkrantz, and G. Stieglbauer, “Organisation and communication problems in automotive requirements engineering,” Requir. Eng., vol. 23, no. 1, pp. 145–167, 2018, doi: 10.1007/s00766-016-0261-7.
[17] Y. Zheng, J. Jiang, W. W. Huang, X. Wu, and H. Ren, “Coordination Resistance in Cross-Functional NPD Projects: The Moderating Role of Temporal Leadership,” IEEE Trans. Eng. Manag., vol. 71, pp. 2138–2152, 2024, doi: 10.1109/TEM.2022.3153188.