Pedestrian Graph +: A Fast Pedestrian Crossing Prediction Model Based on Graph Convolutional Networks

Estimating when pedestrians cross the street is essential for intelligent transportation systems. Accurate, real-time prediction is critical to ensure the safety of the most vulnerable road users while improving passenger comfort. In the present work, we developed a model called Pedestrian Graph +, an improvement of our previous work, Pedestrian Graph, which predicts pedestrian crossing action in urban areas based on a Graph Convolution Network. We integrated two convolutional modules in the new model that provide additional context information (cropped images, cropped segmentation maps, ego-vehicle velocity data) to the main Graph Convolutional module, thus increasing accuracy. Our model is faster and smaller than other state-of-the-art models, achieving equivalent accuracy. Our model is faster than state-of-the-art models, with an inference time of 6 ms (on a GTX 1080) and low memory consumption (0.3 MB). We tested our model on two datasets, Joint Attention in Autonomous Driving (JAAD) and Pedestrian Intention Estimation (PIE), achieving 86% and 89% accuracy, respectively. Another contribution of our work is the ability to dynamically process almost any input size in the time domain without significant loss of accuracy. It is possible due to the fully convolutional property of ConvNets. Our models and results are available at
https://github.com/RodrigoGantier/Pedestrian_graph_plus
.