论文标题
泰坦：使用动作先验的未来预测
TITAN: Future Forecast using Action Priors
论文作者
论文摘要
我们考虑从从移动平台获得的以自我为中心的观点来预测场景代理的未来轨迹的问题。这个问题在各种领域都很重要，特别是对于在导航中做出反应性或战略决策的自主系统中。为了解决这个问题，我们介绍了泰坦（使用目标动作先验网络的轨迹推理），这是一个新模型，该模型结合了先前的位置，动作和上下文，以预测代理商的未来轨迹和未来的自我运动。在没有适当的数据集的情况下，我们创建了泰坦数据集，该数据集由在东京高度互动的城市交通交通场景中从移动车辆中捕获的700个标记的视频剪辑（带有探测率）。我们的数据集包括50个标签，包括车辆状态和动作，行人年龄组以及有针对性的行人行动属性，这些属性在层次上对应于原子，简单/复杂/复杂的文化，运输和交流动作。为了评估我们的模型，我们在Titan数据集上进行了广泛的实验，揭示了针对基准和最先进算法的显着改善。我们还报告了我们的代理重要性机制（AIM）的有希望的结果，该模块通过计算每个代理人对未来的自我trajectory的相对影响，可深入了解感知风险的评估。该数据集可从https://usa.honda-ri.com/titan获得
We consider the problem of predicting the future trajectory of scene agents from egocentric views obtained from a moving platform. This problem is important in a variety of domains, particularly for autonomous systems making reactive or strategic decisions in navigation. In an attempt to address this problem, we introduce TITAN (Trajectory Inference using Targeted Action priors Network), a new model that incorporates prior positions, actions, and context to forecast future trajectory of agents and future ego-motion. In the absence of an appropriate dataset for this task, we created the TITAN dataset that consists of 700 labeled video-clips (with odometry) captured from a moving vehicle on highly interactive urban traffic scenes in Tokyo. Our dataset includes 50 labels including vehicle states and actions, pedestrian age groups, and targeted pedestrian action attributes that are organized hierarchically corresponding to atomic, simple/complex-contextual, transportive, and communicative actions. To evaluate our model, we conducted extensive experiments on the TITAN dataset, revealing significant performance improvement against baselines and state-of-the-art algorithms. We also report promising results from our Agent Importance Mechanism (AIM), a module which provides insight into assessment of perceived risk by calculating the relative influence of each agent on the future ego-trajectory. The dataset is available at https://usa.honda-ri.com/titan
