Predicting future trajectories of surrounding obstacles is a crucial task for
autonomous driving cars to achieve a high degree of road safety. There are
several challenges in trajectory prediction in real-world traffic scenarios,
including obeying traffic rules, dealing with social interactions, handling
traffic of multi-class movement, and predicting multi-modal trajectories with
probability. Inspired by people's natural habit of navigating traffic with
attention to their goals and surroundings, this paper presents a unique dynamic
graph attention network to solve all those challenges. The network is designed
to model the dynamic social interactions among agents and conform to traffic
rules with a semantic map. By extending the anchor-based method to multiple
types of agents, the proposed method can predict multi-modal trajectories with
probabilities for multi-class movements using a single model. We validate our
approach on the proprietary autonomous driving dataset for the logistic
delivery scenario and two publicly available datasets. The results show that
our method outperforms state-of-the-art techniques and demonstrates the
potential for trajectory prediction in real-world traffic.

本文研究自动驾驶汽车预测未来路障轨迹的问题，提出了一种基于动态图注意力网络的方法，针对交通规则、社交互动、多类交通运动等方面的挑战，能够使用单一模型对多模态轨迹进行概率预测，并经过多个数据集的验证，证明该方法具有实际应用潜力且优于现有技术。

自动驾驶车辆的多模态轨迹预测：基于语义地图与动态图注意力网络

Multi-modal Trajectory Prediction for Autonomous Driving with Semantic  Map and Dynamic Graph Attention Network

We propose a motion forecasting model that exploits a novel structured map
representation as well as actor-map interactions. Instead of encoding
vectorized maps as raster images, we construct a lane graph from raw map data
to explicitly preserve the map structure. To capture the complex topology and
long range dependencies of the lane graph, we propose LaneGCN which extends
graph convolutions with multiple adjacency matrices and along-lane dilation. To
capture the complex interactions between actors and maps, we exploit a fusion
network consisting of four types of interactions, actor-to-lane, lane-to-lane,
lane-to-actor and actor-to-actor. Powered by LaneGCN and actor-map
interactions, our model is able to predict accurate and realistic multi-modal
trajectories. Our approach significantly outperforms the state-of-the-art on
the large scale Argoverse motion forecasting benchmark.

通过构建一个从原始地图数据中构造出来的车道图，结合 LaneGCN 和 Actormap 交互设计了一个动态预测模型，可以准确地预测和展示多种行车情景，取得了比现有最好技术更好的表现。