Sensor Fusion and Virtual Sensor Design for Enhanced Multi-Sensor Data Accuracy in Autonomous Systems
Keywords:
Sensor fusion, Virtual sensor, Autonomous systems, Data accuracy, Simulation, Fault toleranceAbstract
Sensor fusion and virtual sensors play a pivotal role in improving data quality for multi-sensor systems used in real-time applications. This paper explores an advanced sensor fusion approach integrating multiple sensor inputs and virtual sensor designs to optimize accuracy and reliability in autonomous systems. By leveraging statistical and machine-learning-based fusion techniques, the proposed method synthesizes redundant and complementary data sources, forming a robust virtual sensor model. This paper investigates the mathematical underpinnings of sensor fusion, proposes a comprehensive simulation framework, and benchmarks two distinct fusion models for their effectiveness. Simulation results validate the capability of the proposed models in enhancing predictive accuracy and resilience against sensor faults, underscoring the method's potential for autonomous applications.
References
[1] Wang, Z., Wu, Y., & Niu, Q. (2019). Multi-sensor fusion in automated driving: A survey. Ieee Access, 8, 2847-2868.
[2] Yeong, D. J., Velasco-Hernandez, G., Barry, J., & Walsh, J. (2021). Sensor and sensor fusion technology in autonomous vehicles: A review. Sensors, 21(6), 2140.
[3] Xiang, C., Feng, C., Xie, X., Shi, B., Lu, H., Lv, Y., ... & Niu, Z. (2023). Multi-sensor fusion and cooperative perception for autonomous driving: A review. IEEE Intelligent Transportation Systems Magazine.
[4] Shahian Jahromi, B., Tulabandhula, T., & Cetin, S. (2019). Real-time hybrid multi-sensor fusion framework for perception in autonomous vehicles. Sensors, 19(20), 4357.
[5] Li, Q., Queralta, J. P., Gia, T. N., Zou, Z., & Westerlund, T. (2020). Multi-sensor fusion for navigation and mapping in autonomous vehicles: Accurate localization in urban environments. Unmanned Systems, 8(03), 229-237.
[6] Du, H., Wang, W., Xu, C., Xiao, R., & Sun, C. (2020). Real-time onboard 3D state estimation of an unmanned aerial vehicle in multi-environments using multi-sensor data fusion. Sensors, 20(3), 919.
[7] Wang, X., Li, K., & Chehri, A. (2023). Multi-sensor fusion technology for 3D object detection in autonomous driving: A review. IEEE Transactions on Intelligent Transportation Systems.
[8] Harris, C. J., Bailey, A., & Dodd, T. J. (1998). Multi-sensor data fusion in defence and aerospace. The Aeronautical Journal, 102(1015), 229-244.
[9] Naeem, W., Sutton, R., & Xu, T. (2012). An integrated multi-sensor data fusion algorithm and autopilot implementation in an uninhabited surface craft. Ocean Engineering, 39, 43-52.
[10] Chen, R., Gevorkian, A., Fung, A., Chen, W. Z., & Raska, V. (2011). Multi-sensor data integration for autonomous sense and avoid. In Infotech@ Aerospace 2011 (p. 1479).
[11] Salameh, N., Challita, G., Mousset, S., Bensrhair, A., & Ramaswamy, S. (2013). Collaborative positioning and embedded multi-sensors fusion cooperation in advanced driver assistance system. Transportation Research Part C: Emerging Technologies, 29, 197-213.
[12] Veysi, P., Adeli, M., & Peirov Naziri, N. Implementation of Kalman Filtering and Multi-Sensor Fusion Data for Autonomous Driving.
[13] Zhong, Z., Hu, Z., Guo, S., Zhang, X., Zhong, Z., & Ray, B. (2022, July). Detecting multi-sensor fusion errors in advanced driver-assistance systems. In proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis (pp. 493-505).
[14] Al-Sharman, M. K., Emran, B. J., Jaradat, M. A., Najjaran, H., Al-Husari, R., & Zweiri, Y. (2018). Precision landing using an adaptive fuzzy multi-sensor data fusion architecture. Applied soft computing, 69, 149-164.
[15] Elsanhoury, M., Koljonen, J., Välisuo, P., Elmusrati, M., & Kuusniemi, H. (2021,September). Survey on recent advances in integrated GNSSs towards seamless navigation using multi-sensor fusion technology. In Proceedings of the 34th international technical meeting of the satellite division of the institute of navigation (ION GNSS+ 2021) (pp. 2754-2765).
[16] Yue, K. (2024). Multi-sensor data fusion for autonomous flight of unmanned aerial vehicles in complex flight environments. Drone Systems and Applications, 12, 1-12.
[17] Yang, G. Z., Andreu-Perez, J., Hu, X., & Thiemjarus, S. (2014). Multi-sensor fusion. In Body sensor networks (pp. 301-354). London: Springer London.
[18] Sumalatha, I. P. P. A., Chaturvedi, P., Patil, S., Thethi, H. P., & Hameed, A. A. (2024, May). Autonomous Multi-Sensor Fusion Techniques for Environmental Perception in Self-Driving Vehicles. In 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE) (pp. 11461151). IEEE.
[19] Mukherjee, M., Banerjee, A., Papadimitriou, A., Mansouri, S. S., & Nikolakopoulos, G. (2021). A decentralized sensor fusion scheme for multi sensorial fault resilient pose estimation. Sensors, 21(24), 8259.
[20] Miah, S., Milonidis, E., Kaparias, I., & Karcanias, N. (2019). An innovative multi-sensor fusion algorithm to enhance positioning accuracy of an instrumented bicycle. IEEE Transactions on Intelligent Transportation Systems, 21(3), 1145-1153.
[21] Lin, K., Li, Y., Sun, J., Zhou, D., & Zhang, Q. (2020). Multi-sensor fusion for body sensor network in medical human–robot interaction scenario. Information Fusion, 57, 15-26.
[22] Liu, W., Liu, Y., & Bucknall, R. (2023). Filtering based multi-sensor data fusion algorithm for a reliable unmanned surface vehicle navigation. Journal of Marine Engineering & Technology, 22(2), 67-83.
[23] Luo, R. C., Yih, C. C., & Su, K. L. (2002). Multisensor fusion and integration: approaches, applications, and future research directions. IEEE Sensors journal, 2(2), 107-119.
[24] Seo, H., Lee, K., & Lee, K. (2023). Investigating the improvement of autonomous vehicle performance through the integration of multi-sensor dynamic mapping techniques. Sensors, 23(5), 2369.
[25] Senel, N., Kefferpütz, K., Doycheva, K., & Elger, G. (2023). Multi-sensor data fusion for real-time multi-object tracking. Processes, 11(2), 501.
