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, leading to suboptimal efficiency or planning failures. To address this issue, we propose a novel multimodal trajectory planning framework that employs a neural network for scene perception, combines 4WIS hybrid A* search to generate a warm start, and utilizes an optimal control problem (OCP) for trajectory optimization. Specifically, a multimodal perception network fusing visual information and vehicle states is employed to capture semantic and contextual scene understanding, enabling the planner to adapt the strategy according to scene complexity (hard or easy task). For hard tasks, guided points are introduced to decompose complex tasks into local subtasks, improving the search efficiency. The multiple steering modes of 4WIS vehicles, Ackermann, diagonal, and zero-turn, are also incorporated as kinematically feasible motion primitives. Moreover, a hierarchical obstacle handling strategy, which categorizes obstacles as "non-traversable", "crossable", and "drive-over", is incorporated into the node expansion process, explicitly linking obstacle attributes to planning actions to enable efficient decisions. 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.

翻译：四轮独立转向（4WIS）车辆因其卓越的机动性而日益受到关注。人类驾驶员通常选择跨越或碾过低矮障碍物（如塑料袋）以高效通过狭窄空间，而现有规划器忽略障碍物属性，导致效率欠佳或规划失败。为解决此问题，我们提出了一种新颖的多模态轨迹规划框架，该框架采用神经网络进行场景感知，结合4WIS混合A*搜索生成热启动，并利用最优控制问题（OCP）进行轨迹优化。具体而言，采用融合视觉信息与车辆状态的多模态感知网络来获取语义和上下文场景理解，使规划器能够根据场景复杂度（困难或简单任务）调整策略。对于困难任务，引入引导点将复杂任务分解为局部子任务，从而提高搜索效率。4WIS车辆的多种转向模式——阿克曼转向、对角转向和零半径转向——也被纳入作为运动学可行的运动基元。此外，将障碍物处理策略分为"不可通过"、"可跨越"和"可碾过"的层次化方法整合到节点扩展过程中，明确将障碍物属性与规划动作关联，以实现高效决策。进一步地，为处理具有运动不确定性的动态障碍物，我们引入了概率风险场模型，构建风险感知驾驶走廊，作为OCP中的线性碰撞约束。实验结果证明了所提框架在生成安全、高效且平滑的4WIS车辆轨迹方面的有效性，尤其在受限环境中。