CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention
This addresses the problem of autonomous vehicle motion planning when map data is unavailable, representing an incremental improvement over existing map-free methods.
The paper tackles vehicle trajectory prediction without map information by proposing CRAT-Pred, which combines crystal graph convolutional neural networks with multi-head self-attention to model social interactions between vehicles. The model achieves state-of-the-art performance among map-free approaches with significantly fewer parameters and demonstrates that self-attention weights can quantitatively represent interaction scores.
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.