Abstract
Autonomous driving in winter weather conditions has always been a unique challenge, and as such it is an interesting research topic. Due to reasons related to safety and local laws, simulators have become one of the first choice for the required research. This paper extends the capabilities of the 3DCoAutoSim simulation platform with a realistic simulation environment for the study of autonomous driving with ROS-controlled vehicles in adverse weather conditions such as snow-covered roads. The weather-related details of the environment such as snow fall and car tracks on the snow were implemented by using Unity3D’s physics and graphics engine. A series of autonomous driving experiments based on behavioral cloning were performed to test the performance of the environment and its scalability for ROS-based machine learning applications. Results from the experiments conducted to validate the approach demonstrated a good driving performance. Moreover, results from the model trained with the data set generated in the snowy environment, showed that car tracks features in the snow promoted the learning and generalization steps in the machine learning process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hussein, A., Díaz-Álvarez, A., Armingol, J.M., Olaverri-Monreal, C.: 3DCoautoSim: Simulator for cooperative ADAS and automated vehicles. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 3014–3019. IEEE (2018)
Hussein, A., García, F., Olaverri-Monreal, C.: Ros and unity based framework for intelligent vehicles control and simulation. In: 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES), pp. 1–6. IEEE (2018)
Artal-Villa, L., Olaverri-Monreal, C.: Vehicle-pedestrian interaction in SUMO and unity3D. In: Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S. (eds.) WorldCIST’19 2019. AISC, vol. 931, pp. 198–207. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16184-2_20
Smirnov, N., Liu, Y., Validi, A., Morales-Alvarez, W., Olaverri-Monreal, C.: A game theory-based approach for modeling autonomous vehicle behavior in congested, urban lane-changing scenarios. Sensors 21(4), 1523 (2021)
Meng, W., Hu, Y., Lin, J., Lin, F., Teo, R.: Ros+ unity: an efficient high-fidelity 3d multi-uav navigation and control simulator in GPS-denied environments. In: IECON 2015-41st Annual Conference of the IEEE Industrial Electronics Society, pp. 002562–002567. IEEE (2015)
Hu, Y., Meng, W.: Rosunitysim: development and experimentation of a real-time simulator for multi-unmanned aerial vehicle local planning. Simulation 92(10), 931–944 (2016)
Katara, P., Khanna, M., Nagar, H., Panaiyappan, A.: Open source simulator for unmanned underwater vehicles using ROS and unity3d. In: 2019 IEEE Underwater Technology (UT), pp. 1–7. IEEE (2019)
Michaeler, F., Olaverri-Monreal, C.: 3D Driving Simulator with VANET capabilities to assess cooperative systems: 3DSimVanet. In: 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 999–1004. IEEE (2017)
Validi, A., Olaverri-Monreal, C.: Simulation-based impact of connected vehicles in platooning mode on travel time, emissions and fuel consumption. arXiv preprint arXiv:2105.10894 (2021)
Liu, Y., Novotny, G., Smirnov, N., Morales-Alvarez, W., Olaverri-Monreal, C.: Mobile delivery robots: Mixed reality-based simulation relying on ROS and unity 3d. In: 2020 IEEE Intelligent Vehicles Symposium (IV), pp. 15–20. IEEE (2020)
Thorstensen, M.C.: Ros bridge lib. https://github.com/MathiasCiarlo/ROSBridgeLib. Accessed 31 Mar 2019
Develter: Simulator and driving on snow. http://www.develter.com/simulator-and-driving-on-snow-_l_EN_r_32_a_36.html. Accessed 3 Nov 2021
CarSim: Mechanical simulation. https://www.carsim.com. Accessed 3 Nov 2021
Carla: Open-source simulator for autonomous driving research. https://carla.org. Accessed 3 Oct 2021
Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. Electron. Imaging 2017(19), 70–76 (2017)
Youssef, A.E., El Missiry, S., El-gaafary, I.N., ElMosalami, J.S., Awad, K.M., Yasser, K.: Building your kingdom imitation learning for a custom gameplay using unity ml-agents. In: 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 0509–0514. IEEE (2019)
Cao, Z., Wong, K., Bai, Q., Lin, C.T.: Hierarchical and non-hierarchical multi-agent interactions based on unity reinforcement learning. In: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, pp. 2095–2097 (2020)
Hossain, S., Lee, D.J.: Autonomous-driving vehicle learning environments using unity real-time engine and end-to-end CNN approach. J. Korea Robot. Soc. 14(2), 122–130 (2019)
Li, X., Cao, Z., Bai, Q.: A novel mountain driving unity simulated environment for autonomous vehicles. In: 35th AAAI Conference on Artificial Intelligence (AAAI) (2021)
Bojarski, M., et al.: End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316 (2016)
Farag, W., Saleh, Z.: Behavior cloning for autonomous driving using convolutional neural networks. In: 2018 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 1–7 (2018)
Koenig, N., Howard, A.: Design and use paradigms for gazebo, an open-source multi-robot simulator. In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), vol. 3, pp. 2149–2154 (2004)
Zamora, I., Lopez, N.G., Vilches, V.M., Cordero, A.H.: Extending the openai gym for robotics: a toolkit for reinforcement learning using ROS and gazebo. arXiv preprint arXiv:1608.05742 (2016)
Lopez, N.G., et al.: gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and gazebo. arXiv preprint arXiv:1903.06278 (2019)
Nuin, Y.L.E., et al.: Ros2learn: a reinforcement learning framework for ROS 2. arXiv preprint arXiv:1903.06282 (2019)
Wiseman, Y.: Real-time monitoring of traffic congestions. In: 2017 IEEE International Conference on Electro Information Technology (EIT), pp. 501–505. IEEE (2017)
Acknowledgment
This work was supported by the Austrian Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK) Endowed Professorship for Sustainable Transport Logistics 4.0., IAV France S.A.S.U., IAV GmbH, Austrian Post AG and the UAS Technikum Wien.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, Y., Morales-Alvarez, W., Novotny, G., Olaverri-Monreal, C. (2022). Study of ROS-Based Autonomous Vehicles in Snow-Covered Roads by Means of Behavioral Cloning Using 3DCoAutoSim. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F. (eds) Information Systems and Technologies. WorldCIST 2022. Lecture Notes in Networks and Systems, vol 470. Springer, Cham. https://doi.org/10.1007/978-3-031-04829-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-031-04829-6_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-04828-9
Online ISBN: 978-3-031-04829-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)