Approaches to speed planning for ground-based autonomous vehicles
- Authors: Livshits A.B.1, Temkin I.O.1, Fadeev A.Y.1
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Affiliations:
- NUST MISIS
- Issue: No 116 (2025)
- Pages: 271-297
- Section: Vehicle control and navigation
- URL: https://ogarev-online.ru/1819-2440/article/view/307008
- ID: 307008
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Abstract
The article provides an overview of speed profile planning methods for ground-based unmanned vehicles. Autonomous driving technology is in the active development phase, and many tasks have already been solved, but with insufficient quality for safe operation and scaling of the technology. Thus, this field is promising from a scientific and engineering point of view. The paper examines in detail the formulation of the problem, describes various types of constraints to the final solution, and considers the construction of the task's functionality, depending on the requirements and methods used. Special attention is paid to the ST graph as a tool for modeling interaction with other traffic participants. Practical scenarios of agent interaction in various situations that often arise in traffic are also analyzed in detail. The authors focus on a class of methods for constructing a velocity profile along a known geometric trajectory. Two families of approaches are considered: dynamic programming and reducing a continuous problem to a quadratic programming problem. The existing modern methods based on these approaches are analyzed in detail, and improvements and improvements are proposed in the context of various additional requirements for the solution. The article serves as a methodological basis for developers of autonomous driving systems.
About the authors
Asya Borisovna Livshits
NUST MISIS
Email: asyalivshits@yandex.ru
Moscow
Igor Olegovich Temkin
NUST MISIS
Email: temkin.io@misis.ru
Moscow
Aleksandr Yur'evich Fadeev
NUST MISIS
Email: frogcatcher@mail.ru
Moscow
References
- AÑON A.M. et al. Multi-profile quadratic programming (MPQP) for optimal gap selection and speed planning of au-tonomous driving // IEEE Int. Conf. on Robotics and Auto-mation (ICRA–2024). – IEEE, 2024. – P. 12158–12164.
- BOYD S.P., VANDENBERGHE L. Convex optimization. – Cambridge University Press, 2004.
- DIACHUK M., EASA S.M. Simultaneous Trajectory and Speed Planning for Autonomous Vehicles Considering Ma-neuver Variants // Applied Sciences. – 2024. – Vol. 14, No. 4. – P. 1579.
- DU Z. et al. Speed profile optimisation for intelligent vehi-cles in dynamic traffic scenarios // Int. Journal of Systems Science. – 2020. – Vol. 51, No. 12. – P. 2167–2180.
- ELBANHAWI M., SIMIC M., JAZAR R. In the passenger seat: investigating ride comfort measures in autonomous cars // IEEE Intelligent Transportation Systems Magazine. – 2015. – Vol. 7, No. 3. – P. 4–17.
- Expanding Waymo’s testing to the city that keeps it weird [Электронный ресурс]. – URL: https://waymo.com/blog/2023/03/expanding-waymos-testing-to-austin/ (дата обра-ще¬ния: 16.03.2025)
- FU D. et al. Drive like a human: Rethinking autonomous driving with large language models // IEEE/CVF Winter Conf. on Applications of Computer Vision Workshops (WACVW–2024). – IEEE, 2024. – P. 910–919.
- KLANČAR G. et al. Drivable path planning using hybrid search algorithm based on E* and Bernstein–Bézier motion primitives // IEEE Trans. on Systems, Man, and Cybernetics: Systems. – 2019. – Vol. 51, No. 8. – P. 4868–4882.
- KRÜGER T.J., GÖHRING D., ULBRICH F. Graph-Based Speed Planning for Autonomous Driving. – PhD thesis, 2019.
- LEE D. et al. Convolution neural network-based lane change intention prediction of surrounding vehicles for ACC // IEEE 20th Int. Conf. on Intelligent Transportation Systems (ITSC–2017). – IEEE, 2017. – P. 1–6.
- LIU C., ZHAN W., TOMIZUKA M. Speed profile planning in dynamic environments via temporal optimization // IEEE Intelligent Vehicles Symposium (IV). – IEEE, 2017. – P. 154–159.
- MARTIN D., LITWHILER D. An investigation of accelera-tion and jerk profiles of public transportation vehicles // An-nual Conference & Exposition. – 2008. – P. 13.194.1–13.194.13.
- MOZAFFARI S. et al. Deep learning-based vehicle behavior prediction for autonomous driving applications: A review // IEEE Trans. on Intelligent Transportation Systems. – 2020. – Vol. 23, No. 1. – P. 33–47.
- SHIMIZU Y. et al. Jerk constrained velocity planning for an autonomous vehicle: Linear programming approach // Int. Conf. on Robotics and Automation (ICRA–2022). – IEEE, 2022. – P. 5814–5820.
- SINGH K.B., SIVARAMAKRISHNAN S. Extended pacejka tire model for enhanced vehicle stability control // arXiv preprint: arXiv:2305.18422. – 2023.
- XU W. Motion planning for autonomous vehicles in urban scenarios: a sequential optimization approach : PhD thesis. – Carnegie Mellon University, 2021.
- YANG G., YAO Y. Vehicle local path planning and time consistency of unmanned driving system based on convolu-tional neural network // Neural Computing and Applications. – 2022. – P. 1–14.
- YEO J., LEE J., JANG K. The effects of rainfall on driving behaviors based on driving volatility // Int. Journal of Sus-tainable Transportation. – 2021. – Vol. 15, No. 6. – P. 435–443.
- ZHANG J., JIN H. Optimized calculation of the economic speed profile for slope driving: Based on iterative dynamic programming // IEEE Trans. on Intelligent Transportation Systems. – 2020. – Vol. 23, No. 4. – P. 3313–3323.
- ZHANG Y. et al. Optimal vehicle path planning using quad-ratic optimization for baidu apollo open platform // IEEE In-telligent Vehicles Symposium (IV). – IEEE, 2020. – P. 978–984.
- ZHAO F. et al. On-Road Trajectory Planning of Connected and Automated Vehicles in Complex Traffic Settings: A Hier-archical Framework of Trajectory Refinement // IEEE Ac-cess. – 2024. – Vol. 12. – P. 7456–7468.
- ZHAO T. et al. Multi-agent tensor fusion for contextual tra-jectory prediction // Proc. of the IEEE/CVF conference on computer vision and pattern recognition. – 2019. – P. 12126–12134.
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