GRIP: Graph-based Interaction-aware Trajectory Prediction

Abstract

Nowadays, autonomous driving cars have become commercially available. However, 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. In this paper, 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. The experimental results on two wellknown public datasets show that our proposed model improves the prediction accuracy of the state-of-the-art solution by 30%. The prediction error of GRIP is one meter shorter than existing schemes. Such an improvement can help autonomous driving cars avoid many traffic accidents. In addition, the proposed GRIP runs 5x faster than the state-of-the-art schemes.

Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review

“…For example, in a traffic jam, knowing that the second vehicle ahead of the TV is accelerating can enable early prediction of speed increase for the TV. Instead of considering a fixed number of vehicles as the SVs, a distance threshold is defined to divide vehicle into the SVs and NVs in [29], [30], [31]. It means that only the interactions of vehicles within this threshold are considered in the prediction model.…”

“…[28] History of states for the TV and nine SVs. [29], [30], [31] A distance threshold is defined to divide vehicles into the SVs and NVs. [32] A soft attention mechanism is used to weight the impact of each observed vehicle.…”

“…• Independent of intended manoeuvre: These approaches predict a unimodal trajectory without explicitly considering the effect of possible manoeuvres on it. The straightforward approach to predict the trajectory of the TV is to estimate the position of it over time [47], [38], [29]. The predictor model can also estimate the displacement of the TV relative to its last position at each step [31], [25].…”

“…[31] Graph Convolutional Network (GCN [50]) and Graph Attention Network (GAT [51]) are used with some adaptations. [29] Graph Convolutional Model is used which consists of several convolutional and graph operation layers.…”

“…They also propose some adaptations to improve the performance of these networks for the vehicle behaviour prediction problem. Li et al [29] propose a graph-based interaction-aware trajectory prediction (GRIP) model. They use a graph convolutional model, which consists of several convolutional layers as well as graph operations, to model the interaction among the vehicles.…”