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arXiv · 2504.05041

Segmented Trajectory Optimization for Autonomous Parking in Unstructured Environments

Hang Yu·Renjie Li
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Abstract

This paper presents a Segmented Trajectory Optimization (STO) method for autonomous parking, which refines an initial trajectory into a dynamically feasible and collision-free one using an iterative SQP-based approach. STO maintains the maneuver strategy of the high-level global planner while allowing curvature discontinuities at switching points to improve maneuver efficiency. To ensure safety, a convex corridor is constructed via GJK-accelerated ellipse shrinking and expansion, serving as safety constraints in each iteration. Numerical simulations in perpendicular and reverse-angled parking scenarios demonstrate that STO enhances maneuver efficiency while ensuring safety. Moreover, computational performance confirms its practicality for real-world applications.

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Hang Yu, Renjie Li. 2025-04-07. Segmented Trajectory Optimization for Autonomous Parking in Unstructured Environments. https://arxiv.org/abs/2504.05041

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