# RAIST: Learning Risk Aware Traffic Interactions via Spatio-Temporal Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2011.08722 · HTML: https://mlsanity.com/paper/2011.08722/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Optimizing Crowd-Aware Multi-Agent Path Finding through Local Communication with Graph Neural Networks](https://mlsanity.com/paper/2309.10275/) (2023)

## Abstract (arXiv)

A key aspect of driving a road vehicle is to interact with other road users, assess their intentions and make risk-aware tactical decisions. An intuitive approach to enabling an intelligent automated driving system would be incorporating some aspects of human driving behavior. To this end, we propose a novel driving framework for egocentric views based on spatio-temporal traffic graphs. The traffic graphs model not only the spatial interactions amongst the road users but also their individual intentions through temporally associated message passing. We leverage a spatio-temporal graph convolutional network (ST-GCN) to train the graph edges. These edges are formulated using parameterized functions of 3D positions and scene-aware appearance features of road agents. Along with tactical behavior prediction, it is crucial to evaluate the risk-assessing ability of the proposed framework. We claim that our framework learns risk-aware representations by improving on the task of risk object identification, especially in identifying objects with vulnerable interactions like pedestrians and cyclists.