  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.
