Pedestrian crossing decision prediction based on behavioral feature using deep learning

Abstract

A pedestrian is classified as a Vulnerable Road User (VRU) due during walking or crossing in the road pedestrian is not protected or shielded. This caused pedestrians to have the most potential risk than other road users, such as motorcycle drivers or car drivers. To support Autonomous vehicles (AV) toward a higher level of independence, AV needs to recognize pedestrian and behavior related to it. Our proposed method utilizes a deep learning approach to predict pedestrian behavior using eight pedestrian input features with three frame values: five frames, ten frames, and 15 frames. Each number of frames is consists of four models, with one hidden layer, two hidden layers, three hidden layers, and four hidden layers. To improve the deep learning model, we conduct hyperparameter tuning, including hidden layer parameters and a number of frame numbers. Our model can predict pedestrians to cross or not cross using eight input features, with the best model using a number of frames values ten combined with three hidden layers. The resulting model prediction accuracy is 94.77%, and the model prediction loss is 0.18%.