Frenet Corridor Planner: An Optimal Local Path Planning Framework for Autonomous Driving

2025-05-06

AI Summary

To address the challenge of simultaneously ensuring path smoothness, safety, and dynamic obstacle avoidance in real-time local path planning for autonomous driving, this paper proposes a drivable corridor modeling and optimization framework based on Frenet coordinates. Methodologically, it introduces a safety-enhanced Frenet-space bounding box and convex-hull obstacle representation to jointly model static and dynamic obstacles geometrically and kinematically; designs a lateral-deviation-driven adaptive drivable corridor generation mechanism; and incorporates an improved spatial-domain bicycle model within a path-velocity decoupled architecture, solved via sequential quadratic programming (SQP) for nonlinear multi-objective optimization. Experimental results demonstrate a 32% improvement in path curvature continuity, a 27% increase in dynamic obstacle avoidance success rate, and an average planning latency of under 50 ms per frame—meeting real-time requirements on embedded automotive platforms.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

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.

Problem

• Ensures smooth and safe navigation around obstacles
• Optimizes path for smoothness and boundary clearance
• Generates real-time adaptive local paths efficiently

Innovation

• Frenet Corridor Planner for local path planning
• Safety-augmented bounding boxes for obstacle modeling
• Space-domain bicycle kinematics for path optimization