用九种驾驶人格生成可控轨迹，让自动驾驶更懂不同司机风格。
PersonaDrive: Controllable Trajectory Prediction with Multi-Dimensional Driving Personas
- 构建双维度驾驶人格数据集，分时紧迫与舒适度各三档共九类。
- 模型能根据语言描述生成对应人格的行驶轨迹，精度优于基线。
- 适合研究可控自动驾驶、个性化行为建模的团队使用。
现有轨迹预测与端到端自动驾驶方法虽提升了城市环境鲁棒性，但缺乏有意义的可控性。现有基准要么无人格标注，要么仅支持单一紧急程度谱（如紧急、正常、放松），无法区分同紧急程度下需不同驾驶动态的人格。为此，我们提出：(i) Persona-Conditioned Trajectory（PCT）数据集，将驾驶人格分解为时间紧迫度与乘车舒适度两个维度，各分三级构成九类人格，每类配以自然语言描述和轨迹；(ii) PersonaDrive框架，可从语言学习人格并生成人格特定轨迹。该框架引入层级锚点重构（PCAT）沿两轴调整锚点，以及BEV级人格融合（PCMF）。训练采用分层引导损失保证轴对齐物理顺序，及轴解耦多样性损失防止对角模式坍缩。实验表明，PersonaDrive在多维场景下持续优于对比基线。代码与数据集见https://github.com/VisualAIKHU/PersonaDrive。
原文摘要 · Abstract (English)
Although recent trajectory prediction and end-to-end autonomous driving methods improve robustness in urban environments, they still lack meaningful controllability. Existing benchmarks either provide no persona-conditioned annotations or support only a single urgency spectrum (i.e., emergency, normal, relaxed), which cannot distinguish personas that share the same urgency level but require different driving dynamics. To address this, we propose (i) the Persona-Conditioned Trajectory (PCT) dataset, which decomposes driving personas along two axes, Temporal Urgency and Ride Comfort, and combines three levels of each to form a grid of nine personas, each paired with natural-language descriptions and trajectories, and (ii) PersonaDrive, a framework that can learn driving personas from language and can generate persona-specific trajectories. PersonaDrive incorporates Persona-Conditioned Anchor Transform (PCAT), which hierarchically reshapes anchors along both axes, and Persona-Conditioned Multi-Modal Fusion (PCMF) for BEV-level persona fusion. Training is supervised by a Hierarchical Guide Loss enforcing axis-aligned physical orderings and an Axis-Decomposed Diversity Loss preventing diagonal mode collapse. Experimental results show that PersonaDrive consistently improves over the compared baselines across multi-dimensional scenarios. The code and PCT dataset are available at https://github.com/VisualAIKHU/PersonaDrive
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。