Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles
Open accessAbstract
Standard-sized autonomous vehicles have rapidly improved thanks to the breakthroughs of deep learning. However, scaling autonomous driving to mini-vehicles poses several challenges due to their limited on-board storage and computing capabilities. Moreover, autonomous systems lack robustness when deployed in dynamic environments where the underlying distribution is different from the distribution learned during training. To address these challenges, we propose a closed-loop learning flow for autonomous driving mini-vehicles that includes the target deployment environment in-the-loop. We leverage a family of compact and high-throughput tinyCNNs to control the mini-vehicle that learn by imitating a computer vision algorithm, i.e., the expert, in the target environment. Thus, the tinyCNNs, having only access to an on-board fast-rate linear camera, gain robustness to lighting conditions and improve over time. Moreover, we introduce an online predictor that can choose between different tinyCNN models at runtime-trading accuracy and latency-which minimises the inference's energy consumption by up to 3.2×. Finally, we leverage GAP8, a parallel ultra-low-power RISC-V-based micro-controller unit (MCU), to meet the real-time inference requirements. When running the family of tinyCNNs, our solution running on GAP8 outperforms any other implementation on the STM32L4 and NXP k64f (traditional single-core MCUs), reducing the latency by over 13× and the energy consumption by 92%.
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In [34], VNN3, VNN4, and LeNet5 models reach an accuracy of 98.74% on reinforced dataset Dset-All (I1, I2, and I3).
“The results obtained VNN3, VNN4, and LeNet5 models reach an accuracy of 98.74% on the reinforced dataset Dset-All (I1, I2, and I3) by using”
From TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications (2022) · cites this paper as [34]
Section 5.1 (page unavailable) reports results for VNN3, VNN4, and LeNet5 after training on a reinforced dataset, but their respective accuracies are 94.1%, 97.4%, and 98.3% on the Dset-1.0 test set—not 98.74% on Dset-All. The same section describes Dset-All as a combined training dataset, distinct from the later reinforced dataset. (LLM, conf=0.91)
Experimental — accuracy not yet measuredThe TinyML approach is deployed on the GAP8 MCI based on the convolution neural network framework.
“It is deployed on the GAP8 MCI based on the convolution neural network framework.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as de Prado et al., 2021
TinyML approach is investigated to seek improvement of autonomous driving of low-scale mini vehicles.
“TinyML approach is investigated to seek for improvement of autonomous driving of such mini vehicles (de Prado et al., 2021).”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as de Prado et al., 2021
The TinyML approach is also tested on the STM32L4 and NXP k64f platforms.
“It is also tested on the STM32L4 and NXP k64f platforms.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as de Prado et al., 2021
Results show that the integration minimizes processing latency by 13x and improves energy consumption by 92%.
“Results show that the integration minimizes processing latency by 13x and improves energy consumption by 92%.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as de Prado et al., 2021
Miguel de Prado et al. [34] enable execution of DL on low-power autonomous driving vehicles with aim to enhance performance.
“Miguel de Prado et al. [34] enable the execution of DL on low-power autonomous driving vehicles with an aim to enhance the performance, e.g., actions/s by learning complex challenges.”
From TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications (2022) · cites this paper as [34]
The mini vehicle consumes only 3.9 lJ per inference.
“It consumes only 3.9 lJ per inference.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as de Prado et al., 2021
Section 5.2.2 reports relative energy savings for CNN inference on the GAP8, but the passages found do not give an absolute energy consumption of 3.9 lJ per inference. Searches for “3.9,” “microjoules per inference,” and Figure 7’s energy measurements found no passage establishing that value. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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Ramkumar, G. (2026) · International Conference on Electronics and Renewable Systems (ICEARS) · doi:10.1109/icears67481.2026.11416596 · Cited by 3 · see citing works
Habiyaremye, J., Zennaro, M., Mikeka, C., & Masabo, E. (2022) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3529836.3529932 · Cited by 3 · see citing works
Realtime Non-invasive Fault Diagnosis of Three-phase Induction Motor
Nguyen, V. K., Tran, V.-K., Nguyen, M. K., Thach, V. T. E., Pham, T. L. H., & Nguyen, C.-N. (2022) · Technical Education Science/Giáo dục Kỹ thuật · doi:10.54644/jte.72b.2022.1231 · Cited by 3 · see citing works
TinyML based Deep Learning Model for Activity Detection
Kayarvizhy, N., Gera, B. M., Sharma, B., & Dakshinamurthy, S. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i11s.8081 · Cited by 3 · see citing works
Zhang, J., Chen, C., Peng, J., & Liang, J. (2022) · International Conference on Information Technology and Intelligent Control (CITIC) · doi:10.1117/12.2653382 · Cited by 3 · see citing works
Empowering voice assistants with TinyML for user-centric innovations and real-world applications
Chittepu, S., Martha, S., & Banik, D. (2025) · Scientific Reports · doi:10.1038/s41598-025-96588-1 · Cited by 2 · see citing works
TinyML: principles and algorithms
Ghorpade, S. N., Chougule, S., Chaudhari, B. S., & Zennaro, M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-322202-3.00009-9 · Cited by 2 · see citing works
RT-MDM: Real-Time Scheduling Framework for Multi-DNN on MCU Using External Memory
Kang, S., Lee, S., Koo, H., Chwa, H. S., & Lee, J. (2024) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/3649329.3655681 · Cited by 2 · see citing works
Datasheets for machine learning sensors
Stewart, M., Zhang, Y., Warden, P., Omri, Y., Prakash, S., Huckelberry, J., Santos, J. H., Hymel, S., Brown, B. Y., MacArthur, J., et al. (2026) · AI Magazine · doi:10.1002/aaai.70050 · Cited by 2 · see citing works
Non-static TinyML for ad hoc networked devices
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Efficient Flexible Edge Inference for Mixed-Precision Quantized DNN using Customized RISC-V Core
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Attention in Motion: Secure Platooning via Transformer-Based Misbehavior Detection
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Qian, Y., Zhang, Y., Fu, S., Shan, K., Zhang, W., & Tang, Z. (2026) · Frontiers in Sustainable Food Systems · doi:10.3389/fsufs.2026.1906757 · Cited by 1 · see citing works
A TinyML Platform for On-Device Continual Learning with Quantized Latent ReplaysPreprint
Ravaglia, L., Rusci, M., Nadalini, D., Capotondi, A., Conti, F., & Luca Benini, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.10486
Self-citationUltralow power communication: Challenges and machine learning solutions
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Decentralised Reputation Tracking using TinyML for Task Recommendation in Spatial Crowdsourcing
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Current Status, Challenges, and Prospects for Machine Vision Empowered by Edge Intelligence
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An overview of machine learning methods on microcontrollers and IoT devices in the TinyML paradigm
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Decomposed kinetics of electric vehicle energy consumption rate: A multi-method approach
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- Authors
- de Prado, M., Rusci, M., Capotondi, A., Donze, R., Benini, L., & Pazos, N.
- Affiliations
- He-Arc Ingenierie, HES-SO, 2800 Delemont, Switzerland
- University of Bologna (IT)
- University of Modena and Reggio Emilia (IT)
- HES-SO University of Applied Sciences and Arts Western Switzerland (CH)
- Integrated System Lab, ETH Zurich, 8092 Zurich, Switzerland
- Corresponding author
- Rusci, Manuele
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-02-13
- Volume
- 21
- Issue
- 4
- Pages
- 1339
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7918899
- OpenAlex
- W3129911950
- Cited by
- 57
- References
- 51
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 13 · 2023: 9 · 2022: 15 · 2021: 3
- FWCI
- 3.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- IRIS UNIMORE (University of Modena and Reggio Emilia) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- ArODES (HES-SO (https://www.hes-so.ch/)) (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Funders
- Horizon 2020 Framework Programme (732204)
- Swiss State Secretariat for Education, Research and Innovation (16.0159)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Control and Systems Engineering › Embedded Systems and FPGA Design
- Keywords
- TinyML, distribution shift robustness, online model selection, energy-efficient inference, ultra-low-power microcontroller, RISC-V, onboard camera, autonomous driving, latency trade-off
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