A profound understanding of inter-agent relationships and motion behaviors is important to achieve high-quality planning when navigating in complex scenarios, especially at urban traffic intersections. We present a trajectory prediction approach with respect to traffic lights, D2-TPred, which uses a spatial dynamic interaction graph (SDG) and a behavior dependency graph (BDG) to handle the problem of discontinuous dependency in the spatial-temporal space. Specifically, the SDG is used to capture spatial interactions by reconstructing sub-graphs for different agents with dynamic and changeable characteristics during each frame. The BDG is used to infer motion tendency by modeling the implicit dependency of the current state on priors behaviors, especially the discontinuous motions corresponding to acceleration, deceleration, or turning direction. Moreover, we present a new dataset for vehicle trajectory prediction under traffic lights called VTP-TL. Our experimental results show that our model achieves more than {20.45% and 20.78% }improvement in terms of ADE and FDE, respectively, on VTP-TL as compared to other trajectory prediction algorithms. The dataset and code are available at: https://github.com/VTP-TL/D2-TPred.
翻译:深入了解机构间关系和运动行为对于在复杂情景下航行时实现高质量规划至关重要,特别是在城市交通交叉点。我们介绍了交通灯D2-TPred的轨迹预测方法,该方法使用空间动态互动图(SDG)和行为依赖图(BDG)处理空间时空空间不连续依赖问题。具体地说,SDG用于通过重建具有动态和可变特性的不同代理人的子图,在每个框架中重建具有动态和可变特性的不同代理人的空间互动。BDG用来通过模拟当前状态对先前行为的隐性依赖性来推导运动趋势,特别是相对于加速、减速或方向转变的不连续动作。此外,我们为交通灯下的车辆轨迹预测提供了一套新的数据集,称为VTP-TL。我们的实验结果显示,我们的模型分别在ADE2和FDE2方面实现了[20.45%和20.78%]以上。与其他轨迹预测算法相比,VTP-TL用于推导动趋势趋势。数据设置和代码见: https://giuth/LTP/D。