  Despite the advancement in the technology of autonomous driving cars, the
safety of a self-driving car is still a challenging problem that has not been
well studied. Motion prediction is one of the core functions of an autonomous
driving car. Previously, we propose a novel scheme called GRIP which is
designed to predict trajectories for traffic agents around an autonomous car
efficiently. GRIP uses a graph to represent the interactions of close objects,
applies several graph convolutional blocks to extract features, and
subsequently uses an encoder-decoder long short-term memory (LSTM) model to
make predictions. Even though our experimental results show that GRIP improves
the prediction accuracy of the state-of-the-art solution by 30%, GRIP still has
some limitations. GRIP uses a fixed graph to describe the relationships between
different traffic agents and hence may suffer some performance degradations
when it is being used in urban traffic scenarios. Hence, in this paper, we
describe an improved scheme called GRIP++ where we use both fixed and dynamic
graphs for trajectory predictions of different types of traffic agents. Such an
improvement can help autonomous driving cars avoid many traffic accidents. Our
evaluations using a recently released urban traffic dataset, namely ApolloScape
showed that GRIP++ achieves better prediction accuracy than state-of-the-art
schemes. GRIP++ ranked #1 on the leaderboard of the ApolloScape trajectory
competition in October 2019. In addition, GRIP++ runs 21.7 times faster than a
state-of-the-art scheme, CS-LSTM.
