The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach
Abstract
This paper presents a novel motion planning and tracking framework for automated vehicles based on artificial potential field (APF) elaborated resistance approach. Motion planning is one of the key parts of autonomous driving, which plans a sequence of movement states to help vehicles drive safely, comfortably, economically, human-like, etc. In this paper, the APF method is used to assign different potential functions to different obstacles and road boundaries; while the drivable area is meshed and assigned resistance values in each edge based on the potential functions. A local current comparison method is employed to find a collision-free path. As opposed to a path, the vehicle motion or trajectory should be planned spatiotemporally. Therefore, the entire planning process is divided into two spaces, namely the virtual and actual. In the virtual space, the vehicle trajectory is predicted and executed step by step over a short horizon with the current vehicle speed. Then, the predicted trajectory is evaluated to decide if the speed should be kept or changed. Finally, it will be sent to the actual space, where an experimentally validated Carsim model controlled by a model predictive controller is used to track the planned trajectory. Several case studies are presented to demonstrate the effectiveness of the proposed framework.
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Cites
Rajamani, R. (2012) · Mechanical Engineering Series · doi:10.1007/978-1-4614-1433-9 · Cited by 4,401
End to End Learning for Self-Driving Cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al. (2016) · arXiv · doi:10.48550/arxiv.1604.07316 · Cited by 3,198
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles
Paden, B., Cap, M., Yong, S. Z., Yershov, D., & Frazzoli, E. (2016) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2016.2578706 · Cited by 2,585
A Review of Motion Planning Techniques for Automated Vehicles
Gonzalez, D., Perez, J., Milanes, V., & Nashashibi, F. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2498841 · Cited by 1,670
Predictive Control for Linear and Hybrid Systems
Borrelli, F., Bemporad, A., & Morari, M. (2017) · Cambridge University Press eBooks · doi:10.1017/9781139061759 · Cited by 1,443
Ji, J., Khajepour, A., Melek, W. W., & Huang, Y. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2555853 · Cited by 1,039
Self-citationModel-Based Predictive Control: A Practical Approach
Rossiter, J. A. (2003) · CERN Document Server (European Organization for Nuclear Research) · Cited by 1,025
A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles
Rasekhipour, Y., Khajepour, A., Chen, S.-K., & Litkouhi, B. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2604240 · Cited by 656
Model predictive control power management strategies for HEVs: A review
Huang, Y., Wang, H., Khajepour, A., He, H., & Ji, J. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.11.106 · Cited by 548
Self-citationPlanning Long Dynamically Feasible Maneuvers for Autonomous Vehicles
Likhachev, M., & Ferguson, D. (2009) · The International Journal of Robotics Research · doi:10.1177/0278364909340445 · Cited by 512
Cited by
Li, G., Yang, Y., Li, S., Qu, X., Lyu, N., & Li, S. E. (2022) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2021.103452 · Cited by 308
Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges
Yao, Q., Tian, Y., Wang, Q., & Wang, S. (2020) · IEEE Access · doi:10.1109/access.2020.3020075 · Cited by 211
Metadata
- Authors
- Huang, Y., Ding, H., Zhang, Y., Wang, H., Cao, D., Xu, N., & Hu, C.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2020-02
- Volume
- 67
- Issue
- 2
- Pages
- 1376-1386
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2912361519
- Cited by
- 409
- References
- 32
- FWCI
- 19.22
- Type
- journal article
- Language
- en
- Open access
- closed
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- Huang, Y., Ding, H., Zhang, Y., Wang, H., Cao, D., Xu, N., & Hu, C. (2020). A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2019.2898599
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