DeepAI AI Chat
Log In Sign Up

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

by   Videsh Suman, et al.

A key aspect of driving a road vehicle is to interact with the other road users, assess their intentions and make risk-aware tactical decisions. An intuitive approach of enabling an intelligent automated driving system would be to incorporate some aspects of the human driving behavior. To this end, we propose a novel driving framework for egocentric views, which is based on spatio-temporal traffic graphs. The traffic graphs not only model the spatial interactions amongst the road users, but also their individual intentions through temporally associated message passing. We leverage 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.


page 1

page 2

page 3

page 4


3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting

Spatio-temporal prediction plays an important role in many application a...

Learning 3D-aware Egocentric Spatial-Temporal Interaction via Graph Convolutional Networks

To enable intelligent automated driving systems, a promising strategy is...

Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs

In this paper, we tackle the problem of spatio-temporal tagging of self-...

Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks

Understanding on-road vehicle behaviour from a temporal sequence of sens...

Understanding Dynamic Scenes using Graph Convolution Networks

We present a novel Multi Relational Graph Convolutional Network (MRGCN) ...

Looking to Relations for Future Trajectory Forecast

Inferring relational behavior between road users as well as road users a...

roadscene2vec: A Tool for Extracting and Embedding Road Scene-Graphs

Recently, road scene-graph representations used in conjunction with grap...