Autonomous parking fundamentally differs from on-road driving due to its frequent direction changes and complex maneuvering requirements. However, existing End-to-End (E2E) planning methods often simplify the parking task into a geometric path regression problem, neglecting explicit modeling of the vehicle's kinematic state. This "dimensionality deficiency" easily leads to physically infeasible trajectories and deviates from real human driving behavior, particularly at critical gear-shift points in multi-shot parking scenarios. In this paper, we propose SunnyParking, a novel dual-branch E2E architecture that achieves motion state awareness by jointly predicting spatial trajectories and discrete motion state sequences (e.g., forward/reverse). Additionally, we introduce a Fourier feature-based representation of target parking slots to overcome the resolution limitations of traditional bird's-eye view (BEV) approaches, enabling high-precision target interactions. Experimental results demonstrate that our framework generates more robust and human-like trajectories in complex multi-shot parking scenarios, while significantly improving gear-shift point localization accuracy compared to state-of-the-art methods. We open-source a new parking dataset of the CARLA simulator, specifically designed to evaluate full prediction capabilities under complex maneuvers.

翻译：自主泊车因其频繁的方向变换和复杂的操控要求，与道路行驶存在根本性差异。然而，现有的端到端规划方法常将泊车任务简化为几何路径回归问题，忽略了对车辆运动学状态的显式建模。这种“维度缺失”易导致物理不可行的轨迹，并偏离真实的人类驾驶行为，尤其是在多段泊车场景中的关键换挡点。本文提出SunnyParking，一种新颖的双分支端到端架构，通过联合预测空间轨迹和离散运动状态序列（如前向/后向）来实现运动状态感知。此外，我们引入了一种基于傅里叶特征的目标泊车位表示方法，以克服传统鸟瞰图方法的分辨率限制，从而实现高精度的目标交互。实验结果表明，我们的框架在复杂的多段泊车场景中能生成更鲁棒且更类人的轨迹，同时在换挡点定位精度上相比现有先进方法有显著提升。我们开源了一个基于CARLA模拟器的新泊车数据集，该数据集专为评估复杂操控下的完整预测能力而设计。