In complex traffic scenarios, intention prediction of surrounding vehicles can improve the strategy of automated driving functions. Existing work on intention prediction is often trained on datasets of single regions or countries. In this article, a transformer network for lane change intention prediction was trained to predict whether target vehicles perform a left lane change, right lane change or keep their lane. Features inputs used were vehicle positions and distances, each for longitudinal and lateral direction. Before being inputted, these features were converted to Frenet coordinates. Two different datasets from leveLXData collected on highways were used: one from German highways and one from Hong Kong highways. Through cross-dataset evaluation, we show that the accuracy values drop to 71.94%, compared to 85.44% when doing dataset-specific training. When training on both datasets, accuracy levels up to 86.84% were achieved.

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