A self-driving solution for resource-constrained autonomous vehicles in parked areas

Abstract

Autonomous vehicles in industrial parks can provide intelligent, efficient, and environmentally friendly transportation services, making them crucial tools for solving internal transportation issues. Considering the characteristics of industrial park scenarios and limited resources, designing and implementing autonomous driving solutions for autonomous vehicles in these areas has become a research hotspot. This paper proposes an efficient autonomous driving solution based on path planning, target recognition, and driving decision-making as its core components. Detailed designs for path planning, lane positioning, driving decision-making, and anti-collision algorithms are presented. Performance analysis and experimental validation of the proposed solution demonstrate its effectiveness in meeting the autonomous driving needs within resource-constrained environments in industrial parks. This solution provides important references for enhancing the performance of autonomous vehicles in these areas.

Keywords

Autonomous vehicles / Path planning / Target recognition / Driving decision-making / Self-driving

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the Natural Science Foundation of Jiangsu Province (BK20211357), the Qing Lan Project of Jiangsu Province (2022), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (22KJB520036 and 23KJB510033), and the Innovation Project of Engineering Research Center of Integration and Application of Digital Learning Technology of MOE (1221046).

References

[1] Sangmin Lee, Sung Ho Park, Concept drift modeling for robust autonomous vehicle control systems in time-varying traffic environments, Expert Syst. Appl. 190 (2022) 116206.

[2] Peng Li, Xiaofei Pei, Zhenfu Chen, Xingzhen Zhou, Jie Xu, Human-like motion planning of autonomous vehicle based on probabilistic trajectory prediction, Appl. Soft Comput. 118 (2022) 108499.

[3] Azzedine Boukerche, Xiren Ma, Vision-based autonomous vehicle recognition: A new challenge for deep learning-based systems, ACM Comput. Surv. 54 (4) (2022) 84:1-84:37.

[4] Liang Zhang, Kefan Wang, Luyuan Xu, Wenjia Sheng, Qi Kang, Evolving ensembles using multi-objective genetic programming for imbalanced classification, Knowledge-Based Systems 255 (2022) 109611.

[5] Yingkun Wen, Lei Liu, Junhuai Li, Xiangwang Hou, Ning Zhang, Mianxiong Dong, Mohammed Atiquzzaman, Kan Wang, Yan Huo, A covert jamming scheme against an intelligent eavesdropper in cooperative cognitive radio networks, IEEE Trans. Veh. Technol. (2023).

[6] Liang Zhang, Qi Kang, Qi Deng, Luyuan Xu, Qidi Wu, A line complex-based evolutionary algorithm for many-objective optimization, IEEE/CAA J. Autom. Sin. 10 (5) (2023) 1150-1167.

[7] Zuobin Xiong, Honghui Xu, Wei Li, Zhipeng Cai, Multi-source adversarial sample attack on autonomous vehicles, IEEE Trans. Veh. Technol. 70 (3) (2021) 2822-2835.

[8] Dier Wang, Jun Zhang, Two improved scanning path planning algorithms and a 3D printing control system with circular motion controller, Rapid Prototyp. J. 28 (4) (2022) 695-703.

[9] Deyi Zhang, Songyong Liu, Xinqing Jia, Yuming Cui, Jian Yao, Full coverage cutting path planning of robotized roadheader to improve cutting stability of the coal lane cross-section containing gangue, Proc. Inst. Mech. Eng. C 236 (1) (2022) 579-592.

[10] Mina Karimi, Zana Zakariyaeinejad, Abolghasem Sadeghi-Niaraki, Ali Hos-seininaveh Ahmadabadian, A new method for automatic and accurate coded target recognition in oblique images to improve augmented reality precision, Trans. GIS 26 (3) (2022) 1509-1530.

[11] Ning Chen, Yimeng Zhang, Jielong Wu, Hongyi Zhang, Vinay Chamola, Victor Hugo C. Brain-computer interface-based target recognition system using transfer learning: A deep learning approach, Comput. Intell. 38 (1) (2022) 139-155.

[12] Héctor Guzmán, MARia E. Larraga, Luis Alvarez-Icaza, et al., A two lanes cellular automata model for traffic flow considering realistic driving decisions, J. Cell. Autom. 10 (2015).

[13] Zhipeng Cai, Zhuojun Duan, Wei Li, Exploiting multi-dimensional task diversity in distributed auctions for mobile crowdsensing, IEEE Trans. Mob. Comput. 20 (8) (2020) 2576-2591.

[14] Jinbao Wang, Zhipeng Cai, Jiguo Yu, Achieving personalized k-anonymity-based content privacy for autonomous vehicles in CPS, IEEE Trans. Ind. Inform. 16 (6) (2019) 4242-4251.

[15] Zhipeng Cai, Xu Zheng, A private and efficient mechanism for data uploading in smart cyber-physical systems, IEEE Trans. Netw. Sci. Eng. 7 (2) (2018) 766-775.

[16] Zhipeng Cai, Xu Zheng, Jiguo Yu, A differential-private framework for urban traffic flows estimation via taxi companies, IEEE Trans. Ind. Inform. 15 (12) (2019) 6492-6499.

[17] Peter E. Hart, Nils J. Nilsson, Bertram Raphael, A formal basis for the heuristic determination of minimum cost paths, IEEE Trans. Syst. Sci. Cybern. 4 (2) (1968) 100-107.

[18] Dirck Van Vliet, Improved shortest path algorithms for transport networks, Transp. Res. 12 (1) (1978) 7-20.

[19] Robert Geisberger, Peter Sanders, Dominik Schultes, Christian Vetter, Exact routing in large road networks using contraction hierarchies, Transp. Sci. 46 (3) (2012) 388-404.

[20] Hannah Bast, Daniel Delling, Andrew Goldberg, Matthias Müller-Hannemann, Thomas Pajor, Peter Sanders, Dorothea Wagner, Renato F. Werneck, Route planning in transportation networks, Algorithm Eng. Sel. Results Surv. (2016) 19-80.

[21] Christopher Urmson, Joshua Anhalt, Michael Clark, Tugrul Galatali, et al., Juan Pablo Gonzalez, Jay Gowdy, Alexander Gutierrez, Sam Harbaugh, Matthew Johnson-Roberson, Hiroki Kato, et al., High Speed Navigation of Unrehearsed Terrain: Red Team Technology for Grand Challenge 2004, Tech. Rep. CMU-RI-TR-04-37, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 2004.

[22] Feihu Zhang, Hauke Stähle, Guang Chen, Chao Chen Carsten Simon, Christian Buckl, Alois Knoll, A sensor fusion approach for localization with cumulative error elimination, in: 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI, IEEE, 2012, pp. 1-6.

[23] Zuobin Xiong, Zhipeng Cai, Qilong Han, Arwa Alrawais, Wei Li, ADGAN: Protect your location privacy in camera data of auto-driving vehicles, IEEE Trans. Ind. Inform. 17 (9) (2020) 6200-6210.

[24] Teddy Ort, Liam Paull, Daniela Rus, Autonomous vehicle navigation in rural environments without detailed prior maps, in: 2018 IEEE International Conference on Robotics and Automation, ICRA, IEEE, 2018, pp. 2040-2047.

[25] Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, Imagenet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst. 25 (2012).

[26] Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, You only look once: Unified, real-time object detection,in:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 779-788.

[27] Glenn Jocher, Manu Sporny, Markus Sabadello, Dave Longley, Christopher Allen, yolov5, W3C Candidate Recommendation Draft, 2021, https://github.com/ultralytics/yolov5.

[28] Daniel Delling, Andrew V. Goldberg, Andreas Nowatzyk, Renato F. Werneck, PHAST: Hardware-accelerated shortest path trees, J. Parallel Distrib. Comput. 73 (7) (2013) 940-952.

[29] Sertac Karaman, Emilio Frazzoli, Sampling-based algorithms for optimal motion planning, Int. J. Robot. Res. 30 (7) (2011) 846-894.

[30] Plamen Petrov, Fawzi Nashashibi, Modeling and nonlinear adaptive control for autonomous vehicle overtaking, IEEE Trans. Intell. Transp. Syst. 15 (4) (2014) 1643-1656.

[31] Ekim Yurtsever, Jacob Lambert, Alexander Carballo, Kazuya Takeda, A survey of autonomous driving: Common practices and emerging technologies, IEEE Access 8 (2020) 58443-58469.