Aspects of autonomous drive control using NVIDIA Jetson Nano microcomputer

• Autorzy: Podbucki Kacper , Marciniak Tomasz

Identyfikatory

DOI

10.15439/2022F89

• Konferencja: Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

The article describes the training process and experiments regarding autonomous movement by the autonomous car Waveshare JetRacer AI. The central unit responsible for controlling the vehicle's systems, i.e. the steering servo and the DC motors used for the drive, is the NVIDIA Jetson Nano embedded device. The application of the IMX219 camera module for data acquisition and training of a neural network models on microcomputer and their use for the implementation of autonomous driving are described.

Słowa kluczowe

EN

training; computational modeling; neural network; process control; microcomputer; data model; trajectory

PL

trening; modelowanie obliczeniowe; sieć neuronowa; kontrola procesu; mikrokomputer; model danych; trajektoria

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 117--120
• Opis fizyczny: Bibliogr. 17 poz., il., tab.

Twórcy

autor

Podbucki Kacper

• Poznan University of Technology, ul. Jana Pawła II 24, 60-965 Poznań, Poland

autor

Marciniak Tomasz

• Poznan University of Technology, ul. Jana Pawła II 24, 60-965 Poznań, Poland

Bibliografia

• 1. Yeong, D.J.; Velasco-Hernandez, G.; Barry, J.; Walsh, J. Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review. Sensors 2021, 21, 2140. https://doi.org/10.3390/s21062140
• 2. Baïou M., Quilliot A., Adouane L., Mombelli A., and Zhu Z., Algorithms for the Safe Management of Autonomous Vehicles, Annals of Computer Science and Information Systems. IEEE, Sep. 26, 2021. http://dx.doi.org/10.15439/2021f18.
• 3. Podbucki K., Possibilities and limitations of environment monitoring with usage of LiDAR scanner, Przegląd Elektrotechniczny, vol. 1, no. 1. Wydawnictwo SIGMA-NOT, sp. z.o.o., pp. 186–189, Jan. 04, 2022. http://dx.doi.org/10.15199/48.2022.01.40.
• 4. SAE International, Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles, SAE J3016 standard, 2021
• 5. Jencoe P., The AV from Algorithm to Acceleration, Under the Digital Hood: Adaptive Computing and AI for Autonomous Vehicles, ElectronicDesign, 2020, pp. 2-7
• 6. Suder, J.; Podbucki, K.; Marciniak, T.; Dąbrowski, A. Low Complexity Lane Detection Methods for Light Photometry System. Electronics 2021, 10, 1665. https://doi.org/10.3390/electronics10141665
• 7. Waveshare, JetRacer AI Kit, AI Racing Robot Powered by Jetson Nano, 17.03.2022, https://www.waveshare.com/jetracer-ai-kit.htm
• 8. NVIDIA Corporation, Jetson Nano Developer Kit,17.03.2022, https://developer.nvidia.com/embedded/jetson-nano-developer-kit
• 9. Madrin F.P.; Rosenberger M.; Nestler R.; Dittrich P-G.; Notni G., The evaluation of CUDA performance on the Jetson Nano board for an image binarization task, Proc. SPIE 11736, Real-Time Image Processing and Deep Learning 2021, 117360G, 12 April 2021, https://doi.org/10.1117/12.2586650
• 10. Waveshare, IMX219 Camera Module, 160 degree FoV, 17.03.2022, https://www.waveshare.com/imx219-d160.htm
• 11. NVIDIA AI IOT, JetCard description, 22.03.2022, https://github.com/NVIDIA-AI-IOT/jetcard
• 12. Świderski A., Wałęsa S., Monitoring of pedestrian crossings using an embedded system, Bachelor's thesis, Supervisor: Tomasz Marciniak, Auxiliary supervisor: Kacper Podbucki, Poznan University of Technology, 2022
• 13. S. Bianco, R. Cadene, L. Celona and P. Napoletano, "Benchmark Analysis of Representative Deep Neural Network Architectures," in IEEE Access, vol. 6, pp. 64270-64277, 2018, http://dx.doi.org/10.1109/ACCESS.2018.2877890.
• 14. Mathworks, Documentation of resnet18, 21.03.2022, https://www.mathworks.com/help/deeplearning/ref/resnet18.html
• 15. NVIDIA AI IOT, Benchmark of torch2rt function, 21.03.2022, https://github.com/NVIDIA-AI-IOT/torch2trt
• 16. Suder, J., Możliwości przetwarzania sekwencji wizyjnych w systemach wbudowanych, Przegląd Elektrotechniczny, No 01/2022, pp. 188-191, https://doi.org/10.15199/48.2022.01.41
• 17. Barreto-Cubero, A.J.; Gómez-Espinosa, A.; Escobedo Cabello, J.A.; Cuan-Urquizo, E.; Cruz-Ramírez, S.R. Sensor Data Fusion for a Mobile Robot Using Neural Networks. Sensors 2022, 22, 305. https://doi.org/10.3390/s22010305

Uwagi

1. Short article

2. Track 1: 17th International Symposium on Advanced Artificial Intelligence in Applications

3. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).