Trajectory representation learning with multilevel attention for driver identification

Massive trajectory data has originated from the development of positioning technology. Learning GPS trajectory representation to characterize a driver's driving style is a challenging task with important applications in many areas, including autonomous driving, auto insurance, advanced driver assistance systems, urban computing, and the internet of things. Few studies have considered the interactions between different factors. In this study, we propose a novel trajectory representation method based on a multilevel attention mechanism (ATTraj2vec) and apply it to the task of driver identification. We use 1D CNN to summarize local features, including motion, spatial and temporal features. Then we utilize a multilevel attention mechanism to extract global features, aggregating the interactions of motion features with temporal and spatial features progressively. Additionally, we adopt multi-loss to optimize our model simultaneously, which consists of a softmax loss for driver classification and Siamese loss for making trajectories from the same driver more similar. Classification experimental results on a real-world automobile trajectory dataset demonstrate that our proposed model significantly outperforms existing baselines. Meanwhile, the proposed method provides significant gains in the trajectory clustering of unseen drivers. The code is available in the repository, https://github.com/ limengyuan2021/ATTraj2vec,