论文标题
基于交互的轨迹预测混合流量图
Interaction-Based Trajectory Prediction Over a Hybrid Traffic Graph
论文作者
论文摘要
交通行为者的行为预测是任何现实世界中自动驾驶系统的重要组成部分。演员的长期行为往往受其与其他演员或交通元素(交通信号灯,停止标志)的互动所支配的。为了捕获这种高度复杂的交互结构,我们建议使用一个混合图,其节点既代表了交通行为者,又代表场景中存在的静态和动态流量元素。参与者和流量元素之间的时间互动的不同模式(例如,停止和行动)是通过图表明确建模的。关于离散互动类型的这种明确的推理不仅有助于预测未来的运动,而且增强了模型的解释性,这对于自动驾驶等安全至关重要的应用非常重要。我们使用图神经网络预测参与者的轨迹和相互作用类型,该网络以半监督方式进行训练。我们表明,我们提出的模型流量图可以达到最新的轨迹预测准确性,同时保持高水平的可解释性。
Behavior prediction of traffic actors is an essential component of any real-world self-driving system. Actors' long-term behaviors tend to be governed by their interactions with other actors or traffic elements (traffic lights, stop signs) in the scene. To capture this highly complex structure of interactions, we propose to use a hybrid graph whose nodes represent both the traffic actors as well as the static and dynamic traffic elements present in the scene. The different modes of temporal interaction (e.g., stopping and going) among actors and traffic elements are explicitly modeled by graph edges. This explicit reasoning about discrete interaction types not only helps in predicting future motion, but also enhances the interpretability of the model, which is important for safety-critical applications such as autonomous driving. We predict actors' trajectories and interaction types using a graph neural network, which is trained in a semi-supervised manner. We show that our proposed model, TrafficGraphNet, achieves state-of-the-art trajectory prediction accuracy while maintaining a high level of interpretability.