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Despite the advancement in the technology of autonomous driving cars, thesafety of a self-driving car is still a challenging problem that has not beenwell studied. Motion prediction is one of the core functions of an autonomousdriving car. Previously, we propose a novel scheme called GRIP which isdesigned to predict trajectories for traffic agents around an autonomous carefficiently. GRIP uses a graph to represent the interactions of close objects,applies several graph convolutional blocks to extract features, andsubsequently uses an encoder-decoder long short-term memory (LSTM) model tomake predictions. Even though our experimental results show that GRIP improvesthe prediction accuracy of the state-of-the-art solution by 30%, GRIP still hassome limitations. GRIP uses a fixed graph to describe the relationships betweendifferent traffic agents and hence may suffer some performance degradationswhen it is being used in urban traffic scenarios. Hence, in this paper, wedescribe an improved scheme called GRIP++ where we use both fixed and dynamicgraphs for trajectory predictions of different types of traffic agents. Such animprovement can help autonomous driving cars avoid many traffic accidents. Ourevaluations using a recently released urban traffic dataset, namely ApolloScapeshowed that GRIP++ achieves better prediction accuracy than state-of-the-artschemes. GRIP++ ranked #1 on the leaderboard of the ApolloScape trajectorycompetition in October 2019. In addition, GRIP++ runs 21.7 times faster than astate-of-the-art scheme, CS-LSTM.

GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving