CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention

TL;DR

CRAT-Pred is a novel vehicle trajectory prediction model that uses crystal graph convolution and self-attention to effectively model social interactions without relying on map data, achieving state-of-the-art results.

Contribution

It introduces a map-free, multi-modal prediction approach combining graph convolution and self-attention, with insights into learned social interactions.

Findings

Achieves state-of-the-art performance with fewer parameters.

Effectively models social interactions without map data.

Self-attention weights correlate with interaction strength.

Abstract

Predicting the motion of surrounding vehicles is essential for autonomous vehicles, as it governs their own motion plan. Current state-of-the-art vehicle prediction models heavily rely on map information. In reality, however, this information is not always available. We therefore propose CRAT-Pred, a multi-modal and non-rasterization-based trajectory prediction model, specifically designed to effectively model social interactions between vehicles, without relying on map information. CRAT-Pred applies a graph convolution method originating from the field of material science to vehicle prediction, allowing to efficiently leverage edge features, and combines it with multi-head self-attention. Compared to other map-free approaches, the model achieves state-of-the-art performance with a significantly lower number of model parameters. In addition to that, we quantitatively show that the self-attention mechanism is able to learn social interactions between vehicles, with the weights representing a measurable interaction score. The source code is publicly available.