A systematic review of hardware technologies for small-scale self-driving cars
DOI:
https://doi.org/10.5902/2179460X84071Keywords:
Autonomous vehicles, Small-scaled vehicles, Self-driving carsAbstract
Autonomous vehicle (AV) technology has the potential to revolutionize the transportation and logistics industry, making it more efficient and safer. However, testing such technologies is often limited by time, space, and cost constraints. Therefore, in recent years, several initiatives have emerged to test autonomous software and hardware on scaled vehicles. In order to provide guidance for future research, this systematic literature review was conducted to provide an overview of the literature surrounding small-scale self-driving cars, summarizing the current autonomous platforms deployed and focusing on the hardware developments in this field. Through the use of databases such as Web of Science, Scopus, Springer Link, Wiley, ACM Digital Library, and the TRID, 38 eligible studies that present small-scale testing of self-driving cars were identified and reviewed. The results indicated that publications on the topic are relatively new, with only the last four years showing an increase in the number of publications. Additionally, most papers only presented preliminary results, highlighting the potential for further research and development in the field. Research papers predominantly focused on software rather than hardware.
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AHN, H. et al. Experimental testing of a semi-autonomous multi-vehicle collision avoidance algorithm at an intersection testbed. In: IEEE International Conference on Intelligent Robots and Systems. [S. l.]: Institute of Electrical and Electronics Engineers Inc., 2015. p. 4834–4839. Disponível em: https://ieeexplore.ieee.org/document/7354056. Acesso em: 25 ago. 2022.
ALCALÁ, E. et al. Autonomous racing using Linear Parameter Varying-Model Predictive Control (LPV-MPC). Control Engineering Practice, [s. l.], v. 95, 2020. Disponível em: https://www.sciencedirect.com/science/article/pii/S0967066119302187?via%3Dihub. Acesso em: 25 ago. 2022.
ANDERT, E.; KHAYATIAN, M.; SHRIVASTAVA, A. Crossroads: Time-Sensitive Autonomous Intersection Management Technique. In: Proceedings - Design Automation Conference. [S. l.]: Institute of Electrical and Electronics Engineers Inc., 2017. Disponível em: https://dl.acm.org/doi/10.1145/3061639.3062221. Acesso em: 25 ago. 2022.
ANINDYAGUNA, K.; BASJARUDDIN, N. C.; SAEFUDIN, D. Overtaking assistant system (OAS) with fuzzy logic method using camera sensor. In: 2016 2nd International Conference of Industrial, Mechanical, Electrical, and Chemical Engineering, ICIMECE 2016. [S. l.]: Institute of Electrical and Electronics Engineers Inc., 2016. p. 89–94. Disponível em: https://ieeexplore.ieee.org/document/7910420. Acesso em: 25 ago. 2022.
BAE, I. et al. Path generation and tracking based on a Bézier curve for a steering rate controller of autonomous vehicles. In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. [S. l.: s. n.], 2013. p. 436–441. Disponível em: https://ieeexplore.ieee.org/document/6728270. Acesso em: 25 ago. 2022.
BAHNIK, M. et al. Visually Assisted Anti-lock Braking System. In: IEEE Intelligent Vehicles Symposium, Proceedings. [S. l.]: Institute of Electrical and Electronics Engineers Inc., 2020. p. 1219–1225. Disponível em: https://ieeexplore.ieee.org/document/9304807. Acesso em: 25 ago. 2022.
BALAJI, B. et al. DeepRacer: Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning. Proceedings - IEEE International Conference on Robotics and Automation, [s. l.], p. 2746–2754, 2020. Disponível em: https://ieeexplore.ieee.org/document/9197465. Acesso em: 25 ago. 2022.
BAUR, M.; BASCETTA, L. An experimentally validated LQR approach to autonomous drifting stabilization. In: 18th European Control Conference, ECC 2019. [S. l.]: Institute of Electrical and Electronics Engineers Inc., 2019. p. 732–737. Disponível em: https://ieeexplore.ieee.org/document/8795883. Acesso em: 25 ago. 2022.
BERNTORP, K. et al. Control Architecture Design for Autonomous Vehicles. In: 2018 IEEE Conference on Control Technology and Applications (CCTA). [S. l.: s. n.], 2018. p. 404–411. Disponível em: https://ieeexplore.ieee.org/document/8511371, Acesso em: 25 ago. 2022.
BETZ, J. et al. Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing. IEEE Open Journal of Intelligent Transportation Systems, [s. l.], v. 3, p. 458–488, 2022. Disponível em: https://arxiv.org/abs/2202.07008. Acesso em: 25 ago. 2022.
BRYAN, W. T.; BOLER, M. E.; BEVLY, D. M. A Vehicle-Independent Autonomous Lane Keeping and Path Tracking System. In: IFAC-PapersOnLine. [S. l.]: Elsevier B.V., 2021. p. 37–44.
CAI, P. et al. Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning. IEEE Robotics and Automation Letters, [s. l.], v. 6, n. 4, p. 7262–7269, 2021. Disponível em: https://ieeexplore.ieee.org/document/9488179. Acesso em: 25 ago. 2022.
CARRAU, J. V. et al. Efficient implementation of Randomized MPC for miniature race cars. In: 2016 European Control Conference, ECC 2016. [S. l.]: Institute of Electrical and Electronics Engineers Inc., 2016. p. 957–962. Disponível em: https://ieeexplore.ieee.org/document/7810413. Acesso em: 25 ago. 2022.
CHOWDHURI, S.; PANKAJ, T.; ZIPSER, K. MultiNet: Multi-modal multi-task learning for autonomous driving. Em: Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019. [S. l.]: Institute of Electrical and Electronics Engineers Inc., 2019. p. 1496–1504. Disponível em: https://ieeexplore.ieee.org/document/8658798. Acesso em: 25 ago. 2022.
DAILY, M. et al. Self-Driving Cars. Computer, [s. l.], v. 50, n. 12, p. 18–23, 2017. Disponível em: https://ieeexplore.ieee.org/document/8220479. Acesso em: 25 ago. 2022.
DO, T.-D. et al. Real-Time Self-Driving Car Navigation Using Deep Neural Network. In: 2018 4th International Conference on Green Technology and Sustainable Development (GTSD). [S. l.]: IEEE, 2018. p. 7–12. Disponível em: https://ieeexplore.ieee.org/document/8595590. Acesso em: 25 ago. 2022.
DREWS, P. et al. Vision-based high-speed driving with a deep dynamic observer. IEEE Robotics and Automation Letters, [s. l.], v. 4, n. 2, p. 1564–1571, 2019. Disponível em: https://ieeexplore.ieee.org/document/8630018. Acesso em: 26 ago. 2022.
FAGNANT, D. J.; KOCKELMAN, K. Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, [s. l.], v. 77, p. 167–181, 2015. Disponível em: https://www.sciencedirect.com/science/article/pii/S0965856415000804?via%3Dihub. Acesso em: 25 ago. 2022.
GOLDFAIN, B. et al. AutoRally: An Open Platform for Aggressive Autonomous Driving. IEEE Control Systems, [s. l.], v. 39, n. 1, p. 26–55, 2019. Disponível em: https://arxiv.org/abs/1806.00678. Acesso em: 25 ago. 2022.
HAMZAH, M. S. et al. Development of Single-board Computer-based Self-Driving Car Model using CNN-Controlled RC Car. In: Proceedings of the International Conference on Electronics and Renewable Systems, ICEARS 2022. [S. l.]: Institute of Electrical and Electronics Engineers Inc., 2022. p. 1805–1812.
HOSSAIN, S. et al. Deep Reinforcement Learning-based ROS-Controlled RC Car for Autonomous Path Exploration in the Unknown Environment. In: International Conference on Control, Automation and Systems. [S. l.]: IEEE Computer Society, 2020. p. 1231–1236. Disponível em: https://ieeexplore.ieee.org/document/9268370. Acesso em: 25 ago. 2022.
HU, Y.; CHEN, H. M.; DELBRUCK, T. Slasher: Stadium racer car for event cameraend-to-end learning autonomous driving experiments. In: 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS). [S. l.: s. n.], 2019. p. 29–33. Disponível em: https://www.zora.uzh.ch/id/eprint/184202/. Acesso em: 25 ago. 2022.
HUSSAIN, R.; ZEADALLY, S. Autonomous Cars: Research Results, Issues, and Future Challenges. IEEE Communications Surveys and Tutorials, [s. l.], v. 21, n. 2, p. 1275–1313, 2019. Disponível em: https://ieeexplore.ieee.org/document/8457076. Acesso em: 25 ago. 2022.
HYLDMAR, N.; HE, Y.; PROROK, A. A Fleet of Miniature Cars for Experiments in Cooperative Driving. In: 2019 International Conference on Robotics and Automation (ICRA). [S. l.: s. n.], 2019. p. 3238–3244. Disponível em: https://ieeexplore.ieee.org/document/8794445. Acesso em: 25 ago. 2022.
IVANOV, R. et al. Case study: Verifying the safety of an autonomous racing car with a neural network controller. In: HSCC 2020 - Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control, part of CPS-IoT Week. [S. l.]: Association for Computing Machinery, Inc, 2020. Disponível em: https://arxiv.org/abs/1910.11309. Acesso em: 25 ago. 2022.
JAHODA, P.; CECH, J.; MATAS, J. Autonomous Car Chasing. In: 16th European Conference on Computer Vision, ECCV 2020. [S. l.: s. n.], 2020. p. 337–352. Disponível em: https://cmp.felk.cvut.cz/ftp/articles/cech/Jahoda-ECCVw-2020.pdf. Acesso em: 25 ago. 2022.
KANNAPIRAN, S.; BERMAN, S. Go-CHART: A miniature remotely accessible self-driving car robot. In: IEEE International Conference on Intelligent Robots and Systems. [S. l.]: Institute of Electrical and Electronics Engineers Inc., 2020. p. 2265–2272. Disponível em: https://ieeexplore.ieee.org/document/9341770. Acesso em: 25 ago. 2022.
KITCHENHAM, B.; CHARTERS, S. Guidelines for performing Systematic Literature Reviews in Software Engineering. Version 2.3. Technical Report EBSE-2007-01, Keele University, U.K., University of Durham, Durham, U.K., 2007.
KLAPALEK, J. et al. Car Racing Line Optimization with Genetic Algorithm using Approximate Homeomorphism. In: IEEE International Conference on Intelligent Robots and Systems. [S. l.]: Institute of Electrical and Electronics Engineers Inc., 2021. p. 601–607. Disponível em: https://ieeexplore.ieee.org/document/9636503. Acesso em: 25 ago. 2022.
KLOESER, D. et al. NMPC for racing using a singularity-free path-parametric model with obstacle avoidance. In: IFAC-PapersOnLine. [S. l.]: Elsevier B.V., 2020. p. 14324–14329. Disponível em: https://www.sciencedirect.com/science/article/pii/S2405896320317845?via%3Dihub. Acesso em: 27 ago. 2022.
LA, H. M. et al. Development of a small-scale research platform for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems, [s. l.], v. 13, n. 4, p. 1753–1762, 2012. Disponível em: https://ieeexplore.ieee.org/document/6248708. Acesso em: 25 ago. 2022.
LAVALLE, S. M. Planning Algorithms. [S. l.]: Cambridge University Press, 2006. doi: 10.1017/CBO9780511546877
LINIGER, A.; LYGEROS, J. Real-Time Control for Autonomous Racing Based on Viability Theory. IEEE Transactions on Control Systems Technology, [s. l.], v. 27, n. 2, p. 464–478, 2019. Disponível em: https://arxiv.org/abs/1701.08735. Acesso em: 25 ago. 2022.
LU, Y. et al. A survey on vision-based UAV navigation. Geo-Spatial Information Science, [s. l.], v. 21, n. 1, p. 21–32, 2018. Disponível em: https://www.tandfonline.com/doi/full/10.1080/10095020.2017.1420509. Acesso em: 25 ago. 2022.
MOURÃO, E. et al. On the performance of hybrid search strategies for systematic literature reviews in software engineering. Information and Software Technology, [s. l.], v. 123, 2020. Disponível em: https://arxiv.org/abs/2004.09741. Acesso em: 27 ago. 2022.
MOZAFFARI, S. et al. Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review. IEEE Transactions on Intelligent Transportation Systems, [s. l.], v. 23, n. 1, p. 33–47, 2022. Disponível em: https://ieeexplore.ieee.org/document/9158529. Acesso em: 25 ago. 2022.
MURALEEDHARAN, A.; OKUDA, H.; SUZUKI, T. Real-Time Implementation of Randomized Model Predictive Control for Autonomous Driving. IEEE Transactions on Intelligent Vehicles, [s. l.], v. 7, n. 1, p. 11–20, 2022. Disponível em: https://ieeexplore.ieee.org/document/9366366. Acesso em: 26 ago. 2022.
O’KELLY, M. et al. F1/10: An Open-Source Autonomous Cyber-Physical Platform. [s. l.], 2019. Disponível em: http://arxiv.org/abs/1901.08567. Acesso em: 26 ago. 2022.
O’KELLY, M. et al. F1TENTH: An Open-source Evaluation Environment for Continuous Control and Reinforcement Learning. Machine Learning Research, [s. l.], v. 123, p. 77–89, 2020a. Disponível em: http://proceedings.mlr.press/v123/o-kelly20a.html. Acesso em: 25 ago. 2022.
O’KELLY, M. et al. TUNERCAR: A Superoptimization Toolchain for Autonomous Racing. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). [S. l.: s. n.], 2020b. p. 5356–5362. Disponível em: https://ieeexplore.ieee.org/document/9197080. Acesso em: 25 ago. 2022.
PAGOT, E.; PICCININI, M.; BIRAL, F. Real-time optimal control of an autonomous RC car with minimum-time maneuvers and a novel kineto-dynamical model. In: IEEE International Conference on Intelligent Robots and Systems. [S. l.]: Institute of Electrical and Electronics Engineers Inc., 2020. p. 2390–2396. Disponível em: https://ieeexplore.ieee.org/document/9340640. Acesso em: 25 ago. 2022.
PENDLETON, S. D. et al. Perception, planning, control, and coordination for autonomous vehicles. Machines, [s. l.], v. 5, n. 1, 2017. Disponível em: https://www.mdpi.com/2075-1702/5/1/6. Acesso em: 25 ago. 2022.
PETERSEN, K. et al. Systematic Mapping Studies in Software Engineering. Proceedings of the 12th international conference on Evaluation and Assessment in Software Engineering, [s. l.], p. 68–77, 2008. Disponível em: https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/EASE2008.8. Acesso em: 27 ago. 2022.
RIBEIRO, A. M. et al. A comprehensive experimental validation of a scaled car-like vehicle: Lateral dynamics identification, stability analysis, and control application. Control Engineering Practice, [s. l.], v. 116, 2021. Disponível em: https://www.sciencedirect.com/science/article/pii/S096706612100201X?via%3Dihub. Acesso em: 25 ago. 2022.
ROSIQUE, F. et al. A systematic review of perception system and simulators for autonomous vehicles research. [S. l.]: MDPI AG, 2019. Disponível em: https://www.mdpi.com/1424-8220/19/3/648. Acesso em: 26 ago. 2022.
ROSOLIA, U.; BORRELLI, F. Learning How to Autonomously Race a Car: A Predictive Control Approach. IEEE Transactions on Control Systems Technology, [s. l.], v. 28, n. 6, p. 2713–2719, 2020. Disponível em: https://ieeexplore.ieee.org/document/8896988. Acesso em: 27 ago. 2022.
SAM, D.; VELANGANNI, C.; EVANGELIN, T. E. A vehicle control system using a time synchronized Hybrid VANET to reduce road accidents caused by human error. Vehicular Communications, [s. l.], v. 6, p. 17–28, 2016. doi: 10.1016/j.vehcom.2016.11.001
SEITZ, S. M. et al. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR’06). [S. l.]: IEEE, 2006. p. 519–528. Disponível em: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1640800. Acesso em: 25 ago. 2022.
SINHA, A. et al. FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis. Em: Proceedings of the 37th International Conference on Machine Learning, PMLR. 119:8992-9004. [S. l.: s. n.], 2020. Disponível em: https://arxiv.org/abs/2003.03900. Acesso em: 27 ago. 2022.
SRINIVASA, S. S. et al. MuSHR: A Low-Cost, Open-Source Robotic Racecar for Education and Research. [s. l.], 2019. Disponível em: https://arxiv.org/abs/1908.08031. Acesso em: 25 ago. 2022.
VASCONCELOS FILHO, E. et al. Towards a Cooperative Robotic Platooning Testbed. In: 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). [S. l.: s. n.], 2020. p. 332–337. Disponível em: https://ieeexplore.ieee.org/document/9096132. Acesso em: 25 ago. 2022.
VEDDER, B.; VINTER, J.; JONSSON, M. A Low-Cost Model Vehicle Testbed with Accurate Positioning for Autonomous Driving. Journal of Robotics, [s. l.], v. 2018, 2018. Disponível em: https://www.hindawi.com/journals/jr/2018/4907536/. Acesso em: 26 ago. 2022.
VERMA, A. et al. Implementation and Validation of Behavior Cloning using Scaled Vehicles. In: SAE Technical Papers 2021. [S. l.: s. n.], 2021. Disponível em: https://saemobilus.sae.org/content/2021-01-0248/. Acesso em: 25 ago. 2022.
WAGENER, N. et al. An Online Learning Approach to Model Predictive Control. In: Robotics: Science and Systems 2019. [S. l.: s. n.], 2019. Disponível em: https://arxiv.org/abs/1902.08967. Acesso em: 28 ago. 2022.
WANG, M. et al. Game-Theoretic Planning for Self-Driving Cars in Multivehicle Competitive Scenarios. IEEE TRANSACTIONS ON ROBOTICS, [s. l.], v. 37, n. 4, p. 1313, 2021. Disponível em: https://ieeexplore.ieee.org/document/9329208. Acesso em: 25 ago. 2022.
WHO. Global status report on road safety 2018: summary. World Health Organization, 2018 (WHO/NMH/NVI/18.20). Licence: CC BY-NC-SA 3.0 IGO). Geneva, Switzerland, 2018.
WILLIAMS, G. et al. Aggressive driving with model predictive path integral control. In: Proceedings - IEEE International Conference on Robotics and Automation. [S. l.]: Institute of Electrical and Electronics Engineers Inc., 2016. p. 1433–1440. Disponível em: https://ieeexplore.ieee.org/document/7487277. Acesso em: 26 ago. 2022.
WILLIAMS, G. et al. Best Response Model Predictive Control for Agile Interactions between Autonomous Ground Vehicles. In: Proceedings - IEEE International Conference on Robotics and Automation. [S. l.]: Institute of Electrical and Electronics Engineers Inc., 2018. p. 2403–2410. Disponível em: https://ieeexplore.ieee.org/document/8462831. Acesso em: 25 ago. 2022.
XU, Z. et al. What drives people to accept automated vehicles? Findings from a field experiment. Transportation Research Part C: Emerging Technologies, [s. l.], v. 95, p. 320–334, 2018. Disponível em: https://www.sciencedirect.com/science/article/pii/S0968090X18302316?via%3Dihub. Acesso em: 25 ago. 2022.
YOU, C.; TSIOTRAS, P. High-Speed Cornering for Autonomous Off-Road Rally Racing. IEEE Transactions on Control Systems Technology, [s. l.], v. 29, n. 2, p. 485–501, 2021. Disponível em: https://ieeexplore.ieee.org/document/8910615. Acesso em: 27 ago. 2022.
ZHANG, T. et al. The roles of initial trust and perceived risk in public’s acceptance of automated vehicles. Transportation Research Part C: Emerging Technologies, [s. l.], v. 98, p. 207–220, 2019. Disponível em: https://www.sciencedirect.com/science/article/pii/S0968090X18308398?via%3Dihub. Acesso em: 25 ago. 2022.
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