提出联合预测方法，显著降低多车场景碰撞率
A Joint Prediction Method of Multi-Agent to Reduce Collision Rate
- 基于SIMPL基线，构建多智能体联合轨迹预测框架
- 在Argoverse 2数据集上碰撞率下降显著
- 适合自动驾驶中多车协同决策场景
预测道路参与者未来运动是实现自动驾驶的关键任务。现有模型大多擅长单一智能体的轨迹预测，但在生成场景内多智能体一致的联合轨迹方面仍面临挑战。以往研究多关注单个智能体的边际预测，而联合预测的重要性日益凸显。联合预测旨在生成整个场景中相互协调的轨迹。本研究在SIMPL基线基础上，探索生成场景一致轨迹的方法。我们在Argoverse 2数据集上测试了该算法，实验结果表明，所提方法能有效生成场景一致的联合轨迹。相比SIMPL基线，本方法显著降低了场景内联合轨迹的碰撞率。
原文摘要 · Abstract (English)
Predicting future motions of road participants is an important task for driving autonomously. Most existing models excel at predicting the marginal trajectory of a single agent, but predicting joint trajectories for multiple agents that are consistent within a scene remains a challenge. Previous research has often focused on marginal predictions, but the importance of joint predictions has become increasingly apparent. Joint prediction aims to generate trajectories that are consistent across the entire scene. Our research builds upon the SIMPL baseline to explore methods for generating scene-consistent trajectories. We tested our algorithm on the Argoverse 2 dataset, and experimental results demonstrate that our approach can generate scene-consistent trajectories. Compared to the SIMPL baseline, our method significantly reduces the collision rate of joint trajectories within the scene.
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。