Lane Change Prediction of Surrounding Vehicles using Video Vision Transformers

Abstract

Anticipating lane changes of surrounding vehicles is paramount for the safe and efficient operation of autonomous vehicles. Previous works employ the usage of physical variables which do not contain contextual information. Recent methodologies relied on action recognition models such as 3D CNNs and RNNs, thereby dealing with complex architecture. Albeit the advent of transformers into action recognition, there are limited works employing transformer architectures. This research addresses the critical challenge of Lane Change Prediction (LCP) for autonomous vehicles, employing Video Action Prediction with a focus on the integration of ViViT (Video Vision Transformers). Utilizing the PREVENTION dataset, which provides detailed annotations of vehicle trajectories and critical events, the proposed approach outperforms prior methods, achieving over 85% test accuracy in predicting lane changes with a horizon of 1 second. Comparative analyses underscore ViViT's superiority in capturing spatio-temporal dependencies in video data while requiring fewer parameters, enhancing computational efficiency. This research contributes to advancing autonomous driving technology by showcasing ViViT's efficacy in real-world applications and advocating for its further exploration in enhancing vehicle safety and efficiency.