将路线信息融入注意力模型，提升自动驾驶预测与规划协同效果。
Prediction-Driven Motion Planning: Route Integration Strategies in Attention-Based Prediction Models
- 在注意力模型中嵌入车辆意图路线与目标位姿
- nuPlan数据集上验证了导航信息显著提升预测与规划性能
- 适合自动驾驶系统融合感知与决策的研究者参考
将运动预测与运动规划相结合，为提升自动驾驶车辆与其他交通参与者交互提供了有前景的框架。然而，这带来了如何基于导航目标来条件化预测，以及确保轨迹具有稳定性和运动学可行性的问题。针对前者挑战，本文研究了在基于注意力的运动预测模型中引入导航信息的扩展方法。通过将本车的预期路线和目标位姿集成到模型架构中，我们弥合了多智能体运动预测与基于目标的运动规划之间的差距。我们在nuPlan数据集上提出了并评估了几种架构级导航信息融合策略。结果表明，预测驱动的运动规划具有潜力，凸显了导航信息对提升预测与规划任务的积极作用。代码实现见：https://github.com/KIT-MRT/future-motion。
原文摘要 · Abstract (English)
Combining motion prediction and motion planning offers a promising framework for enhancing interactions between automated vehicles and other traffic participants. However, this introduces challenges in conditioning predictions on navigation goals and ensuring stable, kinematically feasible trajectories. Addressing the former challenge, this paper investigates the extension of attention-based motion prediction models with navigation information. By integrating the ego vehicle's intended route and goal pose into the model architecture, we bridge the gap between multi-agent motion prediction and goal-based motion planning. We propose and evaluate several architectural navigation integration strategies to our model on the nuPlan dataset. Our results demonstrate the potential of prediction-driven motion planning, highlighting how navigation information can enhance both prediction and planning tasks. Our implementation is at: https://github.com/KIT-MRT/future-motion.
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