Exploiting latent representation of sparse semantic layers for improved short-term motion prediction with Capsule Networks

TL;DR

This paper introduces a novel approach using Capsule Networks to improve short-term motion prediction in autonomous vehicles by leveraging hierarchical semantic layers from HD maps, achieving better accuracy with a smaller model.

Contribution

It presents a new application of Capsule Networks for encoding hierarchical spatial features from sparse semantic map layers in motion prediction.

Findings

Significant improvement over recent methods in deterministic prediction.

Reduces network size while maintaining high accuracy.

Effective hierarchical feature representation with CapsNets.

Abstract

As urban environments manifest high levels of complexity it is of vital importance that safety systems embedded within autonomous vehicles (AVs) are able to accurately anticipate short-term future motion of nearby agents. This problem can be further understood as generating a sequence of coordinates describing the future motion of the tracked agent. Various proposed approaches demonstrate significant benefits of using a rasterised top-down image of the road, with a combination of Convolutional Neural Networks (CNNs), for extraction of relevant features that define the road structure (eg. driveable areas, lanes, walkways). In contrast, this paper explores use of Capsule Networks (CapsNets) in the context of learning a hierarchical representation of sparse semantic layers corresponding to small regions of the High-Definition (HD) map. Each region of the map is dismantled into separate geometrical layers that are extracted with respect to the agent's current position. By using an architecture based on CapsNets the model is able to retain hierarchical relationships between detected features within images whilst also preventing loss of spatial data often caused by the pooling operation. We train and evaluate our model on publicly available dataset nuTonomy scenes and compare it to recently published methods. We show that our model achieves significant improvement over recently published works on deterministic prediction, whilst drastically reducing the overall size of the network.