A Lyapunov Optimization-Based Approach to Autonomous Vehicle Local Path Planning
Ziba Arjmandzadeh, Mohammad Hossein Abbasi, Hanchen Wang, Jiangfeng Zhang, Bin Xu · 2024
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Description
Ziba Arjmandzadeh and co-authors present a path planning method in two thousand twenty-four for self-driving cars based on Lyapunov optimization. Choosing a safe, smooth local path is central to full autonomy, but strong planners can be slow. The team tested their method against model predictive control and a sampling-based planner.
A prototype vehicle gathered real driving data in Norman, Oklahoma, using cameras alone for object detection and bird's-eye coordinates. Each of the three planners then drew paths from the same data. The Lyapunov approach produced safe, smooth paths with at least twenty times less computation than the baselines. The authors argue the speed gain suits real-world deployment where cost and reaction time matter. Vision-only sensing kept the setup cheap and practical.
Catalog record
| CREATOR | Ziba Arjmandzadeh, Mohammad Hossein Abbasi, Hanchen Wang, Jiangfeng Zhang, Bin Xu |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 30 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |