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Using Generative Adversarial Networks and Non-Roadside Video Data to Generate Pedestrian Crossing Scenarios

As fully autonomous driving is introduced on our roads, the safety of vulnerable road users is of the greatest importance. Available real-world data is limited and often lacks the variety required to ensure the safe deployment of new technologies. This paper builds on a novel generation method to generate pedestrian crossing scenarios for autonomous vehicle testing, known as the Ped-Cross GAN. While our previously developed Pedestrian Scenario dataset [1] is extremely detailed, there exist labels in the dataset where available data is severely imbalanced. In this paper, augmented non-roadside data is used to improve the generation results of pedestrians running at the roadside, increasing the classification accuracy from 20.95% to 82.56%, by increasing the training data by only 30%. This proves that researchers can generate rare, edge case scenarios using the Ped-Cross GAN, by successfully supplementing available data with additional non-roadside data. This will allow for adequate testing and greater test coverage when testing the performance of autonomous vehicles in pedestrian crossing scenarios. Ultimately, this will lead to fewer pedestrian casualties on our roads.