Towards a Dynamic Edge AI Framework applied to autonomous driving cars

[EN] This work proposes an innovative solution in the field of Edge AI in order to efficiently exploit new hardware components available on the market at low cost. Edge AI means that algorithms are processed locally on a hardware device. The algorithms use data (sensor data or signals) that are created on the own device. The idea of this paper focuses on demonstrating the validity of the proposed solution by implementing an autonomous driving system that exploits communication between intelligent agents. In this case, our self-driving cars are equipped with a low-cost device that allows you to recognise objects along the way and consequently take actions by running a machine learning model. The presence of a machine learning model also allows the developer to modify it by extending the flexibility and application possibilities of the proposed solution.This work was partly supported by: ERASMUS+ Programme, KA1 Istruzione Superiore, Carta Erasmus+: 29388-EPP-1-2014-1-IT-EPPKA3-ECHE, ACCORDO PER LA MOBILITÀ ERASMUS PER STUDIO - a.a. 2019/2020, Progetto n o 2019-1-IT02-KA103-061203 - CUP: H25J19000080006, Generalitat Valenciana (PROMETEO/2018/002). Universitat Politecnica de Valencia Research Grant PAID-10-19.Muratore, G.; Rincón Arango, JA.; Julian Inglada, VJ.; Carrascosa Casamayor, C.; Greco, G.; Fortino, G. (2020). Towards a Dynamic Edge AI Framework applied to autonomous driving cars. Springer. 406-415. https://doi.org/10.1007/978-3-030-51999-5_34S406415Chang, A.: The role of artificial intelligence in digital health. In: Wulfovich, S., Meyers, A. (eds.) Digital Health Entrepreneurship. HI, pp. 71–81. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-12719-0_7Yang, L., Henthorne, T.L., George, B.: Artificial intelligence and robotics technology in the hospitality industry: current applications and future trends. In: George, B., Paul, J. (eds.) Digital Transformation in Business and Society, pp. 211–228. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-08277-2_13Khayyam, H., Javadi, B., Jalili, M., Jazar, R.N.: Artificial intelligence and internet of things for autonomous vehicles. In: Jazar, R.N., Dai, L. (eds.) Nonlinear Approaches in Engineering Applications, pp. 39–68. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-18963-1_2Li, H., Ota, K., Dong, M.: Learning iot in edge: deep learning for the internet of things with edge computing. IEEE Netw. 32(1), 96–101 (2018)Alonso, R.S., Sittón-Candanedo, I., Rodríguez-González, S., García, Ó., Prieto, J.: A survey on software-defined networks and edge computing over IoT. In: De La Prieta, F., et al. (eds.) PAAMS 2019. CCIS, vol. 1047, pp. 289–301. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24299-2_25Wang, T., Mei, Y., Jia, W., Zheng, X., Wang, G., Xie, M.: Edge-based differential privacy computing for sensor-cloud systems. J. Parallel Distrib. Comput. 136, 75–85 (2020)Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., Zhang, J.: Edge intelligence: paving the last mile of artificial intelligence with edge computing. arXiv preprint arXiv:1905.10083 (2019)Sittón-Candanedo, I., Alonso, R.S., Corchado, J.M., Rodríguez-González, S., Casado-Vara, R.: A review of edge computing reference architectures and a new global edge proposal. Future Gener. Comput. Syst. 99, 278–294 (2019)Ke, R., Zhuang, Y., Pu, Z., Wang, Y.: A smart, efficient, and reliable parking surveillance system with edge artificial intelligence on IoT devices. arXiv preprint arXiv:2001.00269 (2020)Mazzia, V., Khaliq, A., Salvetti, F., Chiaberge, M.: Real-time apple detection system using embedded systems with hardware accelerators: an edge AI application. IEEE Access 8, 9102–9114 (2020)Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. CoRR, abs/1704.04861 (2017)Abadi, M., et al.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). Software available from tensorflow.or

Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions

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Towards Assume-Guarantee Profiles for Autonomous Vehicles

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