Multimodal Classification Network Guided Trajectory Planning for Four-Wheel Independent Steering Autonomous Parking Considering Obstacle Attributes
🤖 AI Summary
Conventional motion planners for autonomous parking of four-wheel independent steering (4WIS) vehicles neglect obstacle traversability (e.g., crossable or crushable obstacles), leading to path failure or inefficiency. Method: This paper proposes a perception-aware hierarchical motion planning framework. First, a multimodal CNN-LSTM network dynamically classifies scene complexity and generates semantic guidance points. Second, a three-tier obstacle-handling strategy is introduced, incorporating crush-speed safety constraints and a probabilistic risk field–driven dynamic collision-avoidance corridor. Third, a hybrid A* planner is coupled with an optimal control problem (OCP) solver, enabling multi-mode node expansion (Ackermann, diagonal, and zero-turning) and heuristic search. Results: Experiments demonstrate significantly improved path success rate and planning efficiency in confined environments, generating safe, smooth, and efficient trajectories. Real-world validation confirms intelligent traversal over low-profile obstacles such as plastic bags.
Application Category
📝 Abstract
Four-wheel Independent Steering (4WIS) vehicles have attracted increasing attention for their superior maneuverability. Human drivers typically choose to cross or drive over the low-profile obstacles (e.g., plastic bags) to efficiently navigate through narrow spaces, while existing planners neglect obstacle attributes, causing inefficiency or path-finding failures. To address this, we propose a trajectory planning framework integrating the 4WIS hybrid A* and Optimal Control Problem (OCP), in which the hybrid A* provides an initial path to enhance the OCP solution. Specifically, a multimodal classification network is introduced to assess scene complexity (hard/easy task) by fusing image and vehicle state data. For hard tasks, guided points are set to decompose complex tasks into local subtasks, improving the search efficiency of 4WIS hybrid A*. The multiple steering modes of 4WIS vehicles (Ackermann, diagonal, and zero-turn) are also incorporated into node expansion and heuristic designs. Moreover, a hierarchical obstacle handling strategy is designed to guide the node expansion considering obstacle attributes, i.e., 'non-traversable', 'crossable', and 'drive-over' obstacles. It allows crossing or driving over obstacles instead of the 'avoid-only' strategy, greatly enhancing success rates of pathfinding. We also design a logical constraint for the 'drive-over' obstacle by limiting its velocity to ensure safety. Furthermore, to address dynamic obstacles with motion uncertainty, we introduce a probabilistic risk field model, constructing risk-aware driving corridors that serve as linear collision constraints in OCP. Experimental results demonstrate the proposed framework's effectiveness in generating safe, efficient, and smooth trajectories for 4WIS vehicles, especially in constrained environments.
Problem
Research questions and friction points this paper is trying to address.
Planning trajectories for 4WIS vehicles in constrained parking environments.
Incorporating obstacle attributes to improve pathfinding success and efficiency.
Handling dynamic obstacles with motion uncertainty for safe navigation.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multimodal network classifies scene complexity for planning
Hierarchical obstacle strategy enables crossing or driving over
Risk field model handles dynamic obstacles with uncertainty
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