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A novel system architecture of intelligent adaptive cruise control for safety aspects

Year 2024, Volume: 13 Issue: 3, 103 - 113, 30.09.2024
https://doi.org/10.18245/ijaet.1406829

Abstract

Following industrial and safety standards for autonomous vehicles, Adaptive Cruise Control (ACC) is a widely employed Advanced Driving Assistance System (ADAS) feature in modern vehicles. ACC currently facilitates speed control based on the driver's desired speed value. This study introduces a significant advancement: the Intelligent Adaptive Cruise Control (IACC) feature, accompanied by the development of a control system architecture poised to make noteworthy contributions in scientific, economic, and social dimensions through its integration into autonomous vehicles. The design incorporates crucial elements such as Traffic Sign and Limit Recognition (TSLR), ADAS features, and Global Positioning System (GPS) data, primarily enhancing driver safety through these supportive features. The main focus revolves around designing a system architecture that accommodates these new features to ensure safe driving. The creation of the IACC system architecture is approached using Model-Based System Engineering (MBSE). Through this MBSE methodology, system-level diagrams were crafted, and security considerations were systematically addressed. Several scenarios were devised to evaluate the contributions and were subsequently tested and analyzed. The architecture places particular emphasis on the security aspects of IACC. Leveraging the TSLR feature, the system interprets traffic signs and acquires speed limit data from external sources, preventing the vehicle's speed from exceeding the specified limit. The comparison between the set speed value and the speed limit ensures adherence to safety parameters.
In such scenarios, the system enhances driver support on winding roads by utilizing GPS data to recognize the vehicle in front. This approach significantly elevates the reliability of the IACC feature, particularly in terms of safety sensitivity, when compared to other adaptive cruise control concepts.

References

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  • M. Vajedi and L. N. Azad, Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control, IEEE Transactions on Intelligent Transportation Systems, vol 1, no. 17, pp. 113-122, 2016.
  • L. Xiao, M. Wang, W. Schakel and B. V. Arem, Unravelling effects of cooperative adaptive cruise control deactivation on traffic flow characteristics at merging bottlenecks, Transportation Research Part C: Emerging Technologies, no. 96, pp. 380-397, 2018.
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  • E. NCAP, Assisted Driving Highway Assist Systems Test & Assessment Protocol, EURO NCAP, 2023.
  • J. Sini and M. Violante, A simulation-based methodology for aiding advanced driver assistance systems hazard analysis and risk assessment, Microelectronics Reliability Elsevier, 11 May 2020.
  • Bosch, Ultrasonic Sensor, [online]. Available: https://www.boschmobility.com/en/solutions/sensors/ultrasonicsensor/. [Accessed: 18.09.2023].
  • Z. Liu, Robust Target Recognition and Tracking of Self-Driving Cars with Radar and Camera Information Fusion Under Severe Weather Conditions, IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 6640 - 6653, 2022.
  • İ. Kılıç, A. Yazıcı, Ö. Yıldız, M. Özçelikors and A. Ondoğan, Intelligent Adaptive Cruise Control System Design and Implementation, 10th Annual System of Systems Engineering Conference, San Antonio, 2015.
  • J. Liu, Z. Yang, Z. Huang, W. Li, S. Dang and H. Li, Simulation Performance Evaluation of Pure Pursuit, Stanley, LQR, MPC Controller for Autonomous Vehicles, in the 2021 IEEE International Conference on Real-time Computing and Robotics, Xining, 2021.
  • C. Madariaga, A. Bashir and C. Swickline, Applying MBSE in Space Based Systems Development, 33rd Annual INCOSE International Symposium Hybrid Event, Honolulu, 2023.
  • M. T. Riaz, S. M. Aaqib, S. Ahmad, S. Amin, H. Ali, S. Husnain and S. Riaz, The Intelligent Transportation Systems with Advanced Technology of Sensor and Network, 2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube), Quetta, 2021.
  • D. Kaslow, B. Ayres, P. T. Cahill, L. Hart and R. Yntema, A Model-Based Systems Engineering (MBSE) approach for defining the behaviors of CubeSats, 2017 IEEE Aerospace Conference, Big Sky, MT, 2017.
  • J. Sini, M. Violante, V. Dodde, R. Gnaniah and L. Pecorella, A Novel Simulation-Based Approach for ISO 26262 Hazard Analysis and Risk Assessment, 25th International Symposium on On-Line Testing and Robust System Design (IOLTS 2019), p. 2, 2019.
  • C. Becker, A. Nasser, J. Brewer and J. A. N. T. S. C. Volpe, Hazard and Safety Analysis of Automated Transit Bus Applications, Federal Transit Administration, Cambridge, 2020.
  • Y. G. Dantas, T. Munaro, C. Carlan, V. Nigam, S. Barner, S. Fan, A. Pretschner, U. Schöpp and S. Tverdyshev, A Model-based System Engineering Plugin for Safety Architecture Pattern Synthesis, 10th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2022), Online Streaming, 2022.
  • S. Khatstgir, H. Sivencrona, S. Birrell, G. Dhadyalla, P. Jennings and P. Billing, Introducing ASIL Inspired Dynamic Tactical Safety Decision Framework for Automated Vehicles, IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, 2017.
  • ISO 15622, Intelligent transport systems Adaptive cruise control systems Performance requirements and test procedures, International Standard ISO 15622, pp. iv-24, 2018.
  • L. Xiao, M. Wang, W. Schakel and B. van Arem, Unravelling effects of cooperative adaptive cruise control deactivation on traffic flow characteristics at merging bottlenecks, Transportation Research Part C: Emerging Technologies vol. 96, pp. 380-397, 2018.
Year 2024, Volume: 13 Issue: 3, 103 - 113, 30.09.2024
https://doi.org/10.18245/ijaet.1406829

Abstract

References

  • Y. Menshenin, Y. Mordecai, E. F. Crawley and B. G. Cameron, "Model-Based System Architecting and Decision-Making," Handbook of Model-Based Systems Engineering, Cambridge, Moscow, Springer, p. 50, 2020.
  • M. Lu, K. Wevers and R. V. D. Heijden, Technical Feasibility of Advanced Driver Assistance Systems (ADAS) for Road Traffic Safety, Transportation Planning and Technology, vol. 28, no. 3, pp. 167-187, 2005.
  • M. Vajedi and L. N. Azad, Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control, IEEE Transactions on Intelligent Transportation Systems, vol 1, no. 17, pp. 113-122, 2016.
  • L. Xiao, M. Wang, W. Schakel and B. V. Arem, Unravelling effects of cooperative adaptive cruise control deactivation on traffic flow characteristics at merging bottlenecks, Transportation Research Part C: Emerging Technologies, no. 96, pp. 380-397, 2018.
  • L. Xiao and F. Gao, A comprehensive review of the development of adaptive cruise control systems, International Journal of Vehicle Mechanics and Mobility, vol. 48, no. 10, pp. 1167-1192, 2010.
  • E. Kural, T. Hacıbekir and B. Aksun Güvenç, State of Art of Adaptive Cruise Control and Stop & Go Systems, 1st AUTOCOM Workshop on Preventive and Active Safety Systems for Road Vehicles, Istanbul, 2020.
  • Front Radar Sensor, Bosch, 2023. [Online]: https://www.boschmobility.com/en/solutions/sensors/frontradar-sensor/. [Accessed: 18.09.2023].
  • E. NCAP, Assisted Driving Highway Assist Systems Test & Assessment Protocol, EURO NCAP, 2023.
  • J. Sini and M. Violante, A simulation-based methodology for aiding advanced driver assistance systems hazard analysis and risk assessment, Microelectronics Reliability Elsevier, 11 May 2020.
  • Bosch, Ultrasonic Sensor, [online]. Available: https://www.boschmobility.com/en/solutions/sensors/ultrasonicsensor/. [Accessed: 18.09.2023].
  • Z. Liu, Robust Target Recognition and Tracking of Self-Driving Cars with Radar and Camera Information Fusion Under Severe Weather Conditions, IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 6640 - 6653, 2022.
  • İ. Kılıç, A. Yazıcı, Ö. Yıldız, M. Özçelikors and A. Ondoğan, Intelligent Adaptive Cruise Control System Design and Implementation, 10th Annual System of Systems Engineering Conference, San Antonio, 2015.
  • J. Liu, Z. Yang, Z. Huang, W. Li, S. Dang and H. Li, Simulation Performance Evaluation of Pure Pursuit, Stanley, LQR, MPC Controller for Autonomous Vehicles, in the 2021 IEEE International Conference on Real-time Computing and Robotics, Xining, 2021.
  • C. Madariaga, A. Bashir and C. Swickline, Applying MBSE in Space Based Systems Development, 33rd Annual INCOSE International Symposium Hybrid Event, Honolulu, 2023.
  • M. T. Riaz, S. M. Aaqib, S. Ahmad, S. Amin, H. Ali, S. Husnain and S. Riaz, The Intelligent Transportation Systems with Advanced Technology of Sensor and Network, 2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube), Quetta, 2021.
  • D. Kaslow, B. Ayres, P. T. Cahill, L. Hart and R. Yntema, A Model-Based Systems Engineering (MBSE) approach for defining the behaviors of CubeSats, 2017 IEEE Aerospace Conference, Big Sky, MT, 2017.
  • J. Sini, M. Violante, V. Dodde, R. Gnaniah and L. Pecorella, A Novel Simulation-Based Approach for ISO 26262 Hazard Analysis and Risk Assessment, 25th International Symposium on On-Line Testing and Robust System Design (IOLTS 2019), p. 2, 2019.
  • C. Becker, A. Nasser, J. Brewer and J. A. N. T. S. C. Volpe, Hazard and Safety Analysis of Automated Transit Bus Applications, Federal Transit Administration, Cambridge, 2020.
  • Y. G. Dantas, T. Munaro, C. Carlan, V. Nigam, S. Barner, S. Fan, A. Pretschner, U. Schöpp and S. Tverdyshev, A Model-based System Engineering Plugin for Safety Architecture Pattern Synthesis, 10th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2022), Online Streaming, 2022.
  • S. Khatstgir, H. Sivencrona, S. Birrell, G. Dhadyalla, P. Jennings and P. Billing, Introducing ASIL Inspired Dynamic Tactical Safety Decision Framework for Automated Vehicles, IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, 2017.
  • ISO 15622, Intelligent transport systems Adaptive cruise control systems Performance requirements and test procedures, International Standard ISO 15622, pp. iv-24, 2018.
  • L. Xiao, M. Wang, W. Schakel and B. van Arem, Unravelling effects of cooperative adaptive cruise control deactivation on traffic flow characteristics at merging bottlenecks, Transportation Research Part C: Emerging Technologies vol. 96, pp. 380-397, 2018.
There are 22 citations in total.

Details

Primary Language English
Subjects Automotive Safety Engineering, Automotive Engineering (Other)
Journal Section Article
Authors

Onur Cihan 0000-0002-5729-2417

Zeynep Musul 0009-0005-5796-5633

Early Pub Date September 29, 2024
Publication Date September 30, 2024
Submission Date December 19, 2023
Acceptance Date July 28, 2024
Published in Issue Year 2024 Volume: 13 Issue: 3

Cite

APA Cihan, O., & Musul, Z. (2024). A novel system architecture of intelligent adaptive cruise control for safety aspects. International Journal of Automotive Engineering and Technologies, 13(3), 103-113. https://doi.org/10.18245/ijaet.1406829
AMA Cihan O, Musul Z. A novel system architecture of intelligent adaptive cruise control for safety aspects. International Journal of Automotive Engineering and Technologies. September 2024;13(3):103-113. doi:10.18245/ijaet.1406829
Chicago Cihan, Onur, and Zeynep Musul. “A Novel System Architecture of Intelligent Adaptive Cruise Control for Safety Aspects”. International Journal of Automotive Engineering and Technologies 13, no. 3 (September 2024): 103-13. https://doi.org/10.18245/ijaet.1406829.
EndNote Cihan O, Musul Z (September 1, 2024) A novel system architecture of intelligent adaptive cruise control for safety aspects. International Journal of Automotive Engineering and Technologies 13 3 103–113.
IEEE O. Cihan and Z. Musul, “A novel system architecture of intelligent adaptive cruise control for safety aspects”, International Journal of Automotive Engineering and Technologies, vol. 13, no. 3, pp. 103–113, 2024, doi: 10.18245/ijaet.1406829.
ISNAD Cihan, Onur - Musul, Zeynep. “A Novel System Architecture of Intelligent Adaptive Cruise Control for Safety Aspects”. International Journal of Automotive Engineering and Technologies 13/3 (September 2024), 103-113. https://doi.org/10.18245/ijaet.1406829.
JAMA Cihan O, Musul Z. A novel system architecture of intelligent adaptive cruise control for safety aspects. International Journal of Automotive Engineering and Technologies. 2024;13:103–113.
MLA Cihan, Onur and Zeynep Musul. “A Novel System Architecture of Intelligent Adaptive Cruise Control for Safety Aspects”. International Journal of Automotive Engineering and Technologies, vol. 13, no. 3, 2024, pp. 103-1, doi:10.18245/ijaet.1406829.
Vancouver Cihan O, Musul Z. A novel system architecture of intelligent adaptive cruise control for safety aspects. International Journal of Automotive Engineering and Technologies. 2024;13(3):103-1.