统一轨迹预测与规划，让模型在不知意图时也能安全决策
PlanTRansformer: Unified Prediction and Planning with Goal-conditioned Transformer
- 用目标条件化的Transformer融合预测与规划，统一建模多智能体交互
- 5秒规划误差降低15.5%，联合指标提升3.5%，优于现有方法
- 适合自动驾驶系统集成，尤其关注动态避障与路径可行性
轨迹预测与规划是自动驾驶中的基础但分离的模块：预测模型需在未知意图下生成多模态运动分布，而规划则依赖已知自身目标生成确定性轨迹。这种不匹配导致预测缺乏意图监督，规划又缺少意图信息。现有预测模型虽基准表现优异，却常脱离规划约束（如避撞、动态可行性）。我们提出Plan TRansformer (PTR)，一种统一的高斯混合Transformer框架，整合目标条件化预测、动态可行性、交互感知与车道级拓扑推理。采用教师-学生训练策略，在训练中逐步掩码周围智能体指令，使其适应推理时意图不可知的场景。在标准数据集上，相比基线Motion Transformer (MTR)，PTR在边缘/联合mAP上分别提升4.3%/3.5%；在5秒预测时域内，规划误差降低15.5%，显著优于GameFormer。该架构无关设计可适配多种Transformer类预测模型。
原文摘要 · Abstract (English)
Trajectory prediction and planning are fundamental yet disconnected components in autonomous driving. Prediction models forecast surrounding agent motion under unknown intentions, producing multimodal distributions, while planning assumes known ego objectives and generates deterministic trajectories. This mismatch creates a critical bottleneck: prediction lacks supervision for agent intentions, while planning requires this information. Existing prediction models, despite strong benchmarking performance, often remain disconnected from planning constraints such as collision avoidance and dynamic feasibility. We introduce Plan TRansformer (PTR), a unified Gaussian Mixture Transformer framework integrating goal-conditioned prediction, dynamic feasibility, interaction awareness, and lane-level topology reasoning. A teacher-student training strategy progressively masks surrounding agent commands during training to align with inference conditions where agent intentions are unavailable. PTR achieves 4.3%/3.5% improvement in marginal/joint mAP compared to the baseline Motion Transformer (MTR) and 15.5% planning error reduction at 5s horizon compared to GameFormer. The architecture-agnostic design enables application to diverse Transformer-based prediction models. Project Website: https://github.com/SelzerConst/PlanTRansformer
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