Data-driven Adaptive ML-enabled Edge-cloud System Framework for Safe and Efficient Autonomous Systems
DOI:
https://doi.org/10.13052/jwe1540-9589.2531Keywords:
ML-enabled systems, autonomous driving systems, edge-cloud computing, adaptive systems, simulation-based testingAbstract
Machine learning (ML)-enabled systems like autonomous driving systems (ADSs) face challenges meeting safety and performance requirements in diverse environments, especially in resource-constrained, latency-sensitive edge-cloud settings. These challenges often arise from the ML models’ limitations, including poor generalization to unseen conditions. Static ML models often struggle to generalize to unseen scenarios, particularly under the latency and resource constraints of edge-cloud infrastructure. Adaptive algorithms using ML system switching have been proposed, but existing approaches frequently lack generalizability, support for common black-box systems, and effective use of distributed edge-cloud resources. This paper presents a novel adaptive ML-enabled edge-cloud system framework to address these shortcomings. Our framework combines cloud-based pre-runtime analysis, which leverages simulation for behavioral understanding and scenario-to-system mapping, with collaborative edge-cloud runtime adaptation featuring dynamic ML model switching. It supports black-box systems and aims to balance safety and efficiency by utilizing appropriate edge and cloud resources situationally. Preliminary CARLA-based evaluation of the edge runtime component suggests our framework can potentially improve the safety-efficiency trade-off compared to single-model ADSs in some scenarios. Moreover, extensive experiments using the MetaDrive simulator with 100,000 randomized driving scenarios demonstrate that the adaptive system improves safety by 2.6% while doubling computational efficiency compared to a single-model baseline. These results validate the framework’s scalability and the feasibility of data-driven scenario–system mapping for adaptive ML-enabled autonomous systems operating across edge and cloud environments.
Downloads
References
Baidu apollo team (2017), apollo: Open source autonomous driving. https://github.com/ApolloAuto/apollo. Accessed: 2024-12-09.
Carla autonomous driving leaderboard. https://leaderboard.carla.org/. Accessed: 2024-12-09.
Replication kit. https://github.com/EunhoCho/AdaptiveADS/. Accessed: 2025-04-17.
Replication kit. https://github.com/EunhoCho/MetadriveADS/. Accessed: 2025-10-31.
Maral Amir, Frank Vahid, and Tony Givargis. Switching predictive control using reconfigurable state-based model. ACM transactions on Design Automation of Electronic systems (tODAEs), 24(1):1–21, 2018.
Kashyap Chitta, Aditya Prakash, Bernhard Jaeger, Zehao Yu, Katrin Renz, and Andreas Geiger. Transfuser: Imitation with transformer-based sensor fusion for autonomous driving. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11):12878–12895, 2023.
Eunho Cho and In-Young Ko. Adaptive ml-enabled edge-cloud system framework for safe and efficient autonomous systems. In 5th International Workshop on Big data driven Edge Cloud Services (BECS 2025) Co-located with the 25th International Conference on Web Engineering (ICWE 2025), 2025.
Eunho Cho, Gwangoo Yeo, Eunkyoung Jee, and Doo-Hwan Bae. Anomaly-aware adaptation approach for self-adaptive cyber-physical system of systems using reinforcement learning. In 2022 17th Annual System of Systems Engineering Conference (SOSE), pages 7–12. IEEE, 2022.
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. Carla: An open urban driving simulator. In Conference on robot learning, pages 1–16. PMLR, 2017.
Fitash Ul Haq, Donghwan Shin, and Lionel Briand. Efficient online testing for dnn-enabled systems using surrogate-assisted and many-objective optimization. In Proceedings of the 44th international conference on software engineering, pages 811–822. IEEE, 2022.
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou. Deep learning scaling is predictable, empirically. arXiv preprint arXiv:1712.00409, 2017.
Xiaofeng Ji, Faming Gong, Nuanlai Wang, Junjie Xu, and Xing Yan. Cloud-edge collaborative service architecture with large-tiny models based on deep reinforcement learning. IEEE Transactions on Cloud Computing, 2025.
Shinpei Kato, Shota Tokunaga, Yuya Maruyama, Seiya Maeda, Manato Hirabayashi, Yuki Kitsukawa, Abraham Monrroy, Tomohito Ando, Yusuke Fujii, and Takuya Azumi. Autoware on board: Enabling autonomous vehicles with embedded systems. In 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS), pages 287–296. IEEE, 2018.
Jeffrey O Kephart and David M Chess. The vision of autonomic computing. Computer, 36(1):41–50, 2003.
Shubham Kulkarni, Arya Marda, and Karthik Vaidhyanathan. Towards self-adaptive machine learning-enabled systems through qos-aware model switching. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE), pages 1721–1725. IEEE, 2023.
Quanyi Li, Zhenghao Peng, Lan Feng, Qihang Zhang, Zhenghai Xue, and Bolei Zhou. Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
Khan Muhammad, Amin Ullah, Jaime Lloret, Javier Del Ser, and Victor Hugo C de Albuquerque. Deep learning for safe autonomous driving: Current challenges and future directions. IEEE Transactions on Intelligent Transportation Systems, 22(7):4316–4336, 2020.
Hirofumi Noguchi, Takuma Isoda, and Seisuke Arai. Shared trained models selection and management for transfer reinforcement learning in open iot. In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 2170–2176. IEEE, 2021.
Zhenghao Peng, Quanyi Li, Chunxiao Liu, and Bolei Zhou. Safe driving via expert guided policy optimization. In 5th Annual Conference on Robot Learning, 2021.
Alexandru Constantin Serban. Designing safety critical software systems to manage inherent uncertainty. In 2019 IEEE International Conference on Software Architecture Companion (ICSA-C), pages 246–249. IEEE, 2019.
Sina Shafaei, Stefan Kugele, Mohd Hafeez Osman, and Alois Knoll. Uncertainty in machine learning: A safety perspective on autonomous driving. In Computer Safety, Reliability, and Security: SAFECOMP 2018 Workshops, ASSURE, DECSoS, SASSUR, STRIVE, and WAISE, Västerås, Sweden, September 18, 2018, Proceedings 37, pages 458–464. Springer, 2018.
Hao Shao, Letian Wang, Ruobing Chen, Hongsheng Li, and Yu Liu. Safety-enhanced autonomous driving using interpretable sensor fusion transformer. In Proceedings of The 6th Conference on Robot Learning, volume 205, pages 726–737. PMLR, 2023.
Sepehr Sharifi, Donghwan Shin, Lionel C. Briand, and Nathan Aschbacher. Identifying the hazard boundary of ml-enabled autonomous systems using cooperative coevolutionary search. IEEE Transactions on Software Engineering, 49(12):5120–5138, 2023.
Chellammal Surianarayanan, John Jeyasekaran Lawrence, Pethuru Raj Chelliah, Edmond Prakash, and Chaminda Hewage. A survey on optimization techniques for edge artificial intelligence (ai). Sensors, 23(3):1279, 2023.
Jie M Zhang, Mark Harman, Lei Ma, and Yang Liu. Machine learning testing: Survey, landscapes and horizons. IEEE Transactions on Software Engineering, 48(1):1–36, 2020.
