Frenet Corridor Planner: An Optimal Local Path Planning Framework for Autonomous Driving
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912362828660736 |
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| author | Tariq, Faizan M. Yeh, Zheng-Hang Singh, Avinash Isele, David Bae, Sangjae |
| author_facet | Tariq, Faizan M. Yeh, Zheng-Hang Singh, Avinash Isele, David Bae, Sangjae |
| contents | Motivated by the requirements for effectiveness and efficiency, path-speed decomposition-based trajectory planning methods have widely been adopted for autonomous driving applications. While a global route can be pre-computed offline, real-time generation of adaptive local paths remains crucial. Therefore, we present the Frenet Corridor Planner (FCP), an optimization-based local path planning strategy for autonomous driving that ensures smooth and safe navigation around obstacles. Modeling the vehicles as safety-augmented bounding boxes and pedestrians as convex hulls in the Frenet space, our approach defines a drivable corridor by determining the appropriate deviation side for static obstacles. Thereafter, a modified space-domain bicycle kinematics model enables path optimization for smoothness, boundary clearance, and dynamic obstacle risk minimization. The optimized path is then passed to a speed planner to generate the final trajectory. We validate FCP through extensive simulations and real-world hardware experiments, demonstrating its efficiency and effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03695 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Frenet Corridor Planner: An Optimal Local Path Planning Framework for Autonomous Driving Tariq, Faizan M. Yeh, Zheng-Hang Singh, Avinash Isele, David Bae, Sangjae Robotics Systems and Control Motivated by the requirements for effectiveness and efficiency, path-speed decomposition-based trajectory planning methods have widely been adopted for autonomous driving applications. While a global route can be pre-computed offline, real-time generation of adaptive local paths remains crucial. Therefore, we present the Frenet Corridor Planner (FCP), an optimization-based local path planning strategy for autonomous driving that ensures smooth and safe navigation around obstacles. Modeling the vehicles as safety-augmented bounding boxes and pedestrians as convex hulls in the Frenet space, our approach defines a drivable corridor by determining the appropriate deviation side for static obstacles. Thereafter, a modified space-domain bicycle kinematics model enables path optimization for smoothness, boundary clearance, and dynamic obstacle risk minimization. The optimized path is then passed to a speed planner to generate the final trajectory. We validate FCP through extensive simulations and real-world hardware experiments, demonstrating its efficiency and effectiveness. |
| title | Frenet Corridor Planner: An Optimal Local Path Planning Framework for Autonomous Driving |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2505.03695 |