Using meta-learning to establish a highly transferable driving speed prediction model from the visual road environment

Abstract

Driving speed prediction is of great significance for the realization of autonomous driving and intelligent transportation systems. The effectiveness of the existing driving speed prediction models often relies on a large number of training data, and their performance will drop sharply with the change in the usage environment. To solve these problems, this study adopted a Meta-learning algorithm to propose a highly transferable driving speed prediction model based on the visual road environment. The data used in this study were the visual road environment pictures and corresponding driving speed from three different types of scenarios (i.e., rural roads, urban roads, and highways) in a naturalistic driving experiment and a public KITTI dataset. The analysis of a Random forest model showed that the influence degree of visual road environment elements on driving speed was quite distinct in different scenarios. Then, a Meta-learning-based driving speed prediction model was presented to address two issues, including the small sample size and transferability. To examine the performance of this new model, a driving speed prediction model based on a convolutional neural network (CNN) was also used for comparison. The results showed that the prediction accuracy of Meta-learning was significantly improved compared to CNN in the face of the small sample size problem and when transferred to new scenarios. The findings in this study can contribute to the optimization of the driving speed prediction model and the improvement of the visual road environment design.