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.
Downloads
References
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. DOI: https://doi.org/10.1109/IROS.2015.7354056
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. DOI: https://doi.org/10.1016/j.conengprac.2019.104270
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. DOI: https://doi.org/10.1145/3061639.3062221
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. DOI: https://doi.org/10.1109/ICIMECE.2016.7910420
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. DOI: https://doi.org/10.1109/ITSC.2013.6728270
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. DOI: https://doi.org/10.1109/IV47402.2020.9304807
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. DOI: https://doi.org/10.1109/ICRA40945.2020.9197465
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. DOI: https://doi.org/10.23919/ECC.2019.8795883
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. DOI: https://doi.org/10.1109/CCTA.2018.8511371
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. DOI: https://doi.org/10.1109/OJITS.2022.3181510
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. DOI: https://doi.org/10.1016/j.ifacol.2021.06.006
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. DOI: https://doi.org/10.1109/LRA.2021.3097345
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. DOI: https://doi.org/10.1109/ECC.2016.7810413
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. DOI: https://doi.org/10.1109/WACV.2019.00164
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. DOI: https://doi.org/10.1109/MC.2017.4451204
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. DOI: https://doi.org/10.1109/GTSD.2018.8595590
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. DOI: https://doi.org/10.1109/LRA.2019.2896449
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. DOI: https://doi.org/10.1016/j.tra.2015.04.003
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. DOI: https://doi.org/10.1109/MCS.2018.2876958
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. DOI: https://doi.org/10.1109/ICEARS53579.2022.9751873
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. DOI: https://doi.org/10.23919/ICCAS50221.2020.9268370
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. DOI: https://doi.org/10.1109/AICAS.2019.8771520
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. DOI: https://doi.org/10.1109/COMST.2018.2869360
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. DOI: https://doi.org/10.1109/ICRA.2019.8794445
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. DOI: https://doi.org/10.1145/3365365.3382216
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. DOI: https://doi.org/10.1007/978-3-030-66823-5_20
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. DOI: https://doi.org/10.1109/IROS45743.2020.9341770
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. DOI: https://doi.org/10.1109/IROS51168.2021.9636503
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. DOI: https://doi.org/10.1016/j.ifacol.2020.12.1376
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. DOI: https://doi.org/10.1109/TITS.2012.2207454
LAVALLE, S. M. Planning Algorithms. [S. l.]: Cambridge University Press, 2006. doi: 10.1017/CBO9780511546877 DOI: https://doi.org/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. DOI: https://doi.org/10.1109/TCST.2017.2772903
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. DOI: https://doi.org/10.1080/10095020.2017.1420509
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. DOI: https://doi.org/10.1016/j.infsof.2020.106294
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. DOI: https://doi.org/10.1109/TITS.2020.3012034
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. DOI: https://doi.org/10.1109/TIV.2021.3062730
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. DOI: https://doi.org/10.1109/ICRA40945.2020.9197080
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. DOI: https://doi.org/10.1109/IROS45743.2020.9340640
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. DOI: https://doi.org/10.3390/machines5010006
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. DOI: https://doi.org/10.1016/j.conengprac.2021.104924
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. DOI: https://doi.org/10.3390/s19030648
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. DOI: https://doi.org/10.1109/TCST.2019.2948135
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 DOI: https://doi.org/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. DOI: https://doi.org/10.1109/CVPR.2006.19
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. DOI: https://doi.org/10.1109/ICARSC49921.2020.9096132
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. DOI: https://doi.org/10.1155/2018/4907536
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. DOI: https://doi.org/10.1109/TRO.2020.3047521
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. DOI: https://doi.org/10.1109/ICRA.2016.7487277
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. DOI: https://doi.org/10.1109/ICRA.2018.8462831
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. DOI: https://doi.org/10.1016/j.trc.2018.07.024
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. DOI: https://doi.org/10.1109/TCST.2019.2950354
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. DOI: https://doi.org/10.1016/j.trc.2018.11.018
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Ciência e Natura
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
To access the DECLARATION AND TRANSFER OF COPYRIGHT AUTHOR’S DECLARATION AND COPYRIGHT LICENSE click here.
Ethical Guidelines for Journal Publication
The Ciência e Natura journal is committed to ensuring ethics in publication and quality of articles.
Conformance to standards of ethical behavior is therefore expected of all parties involved: Authors, Editors, Reviewers, and the Publisher.
In particular,
Authors: Authors should present an objective discussion of the significance of research work as well as sufficient detail and references to permit others to replicate the experiments. Fraudulent or knowingly inaccurate statements constitute unethical behavior and are unacceptable. Review Articles should also be objective, comprehensive, and accurate accounts of the state of the art. The Authors should ensure that their work is entirely original works, and if the work and/or words of others have been used, this has been appropriately acknowledged. Plagiarism in all its forms constitutes unethical publishing behavior and is unacceptable. Submitting the same manuscript to more than one journal concurrently constitutes unethical publishing behavior and is unacceptable. Authors should not submit articles describing essentially the same research to more than one journal. The corresponding Author should ensure that there is a full consensus of all Co-authors in approving the final version of the paper and its submission for publication.
Editors: Editors should evaluate manuscripts exclusively on the basis of their academic merit. An Editor must not use unpublished information in the editor's own research without the express written consent of the Author. Editors should take reasonable responsive measures when ethical complaints have been presented concerning a submitted manuscript or published paper.
Reviewers: Any manuscripts received for review must be treated as confidential documents. Privileged information or ideas obtained through peer review must be kept confidential and not used for personal advantage. Reviewers should be conducted objectively, and observations should be formulated clearly with supporting arguments, so that Authors can use them for improving the paper. Any selected Reviewer who feels unqualified to review the research reported in a manuscript or knows that its prompt review will be impossible should notify the Editor and excuse himself from the review process. Reviewers should not consider manuscripts in which they have conflicts of interest resulting from competitive, collaborative, or other relationships or connections with any of the authors, companies, or institutions connected to the papers.
