Lane Change Intention Prediction of two distinct Populations using a Transformer
This work addresses the problem of cross-population generalization in lane change prediction for autonomous driving, highlighting a critical limitation in existing methods.
The study tested a transformer model for lane change intention prediction on two distinct datasets from Germany and Hong Kong, finding that accuracy dropped to 39.43% when trained on one population and tested on another, but improved to 86.71% when trained on both populations simultaneously.
As a result of the growing importance of lane change intention prediction for a safe and efficient driving experience in complex driving scenarios, researchers have in recent years started to train novel machine learning algorithms on available datasets with promising results. A shortcoming of this recent research effort, though, is that the vast majority of the proposed algorithms are trained on a single datasets. In doing so, researchers failed to test if their algorithm would be as effective if tested on a different dataset and, by extension, on a different population with respect to the one on which they were trained. In this article we test a transformer designed for lane change intention prediction on two datasets collected by LevelX in Germany and Hong Kong. We found that the transformer's accuracy plummeted when tested on a population different to the one it was trained on with accuracy values as low as 39.43%, but that when trained on both populations simultaneously it could achieve an accuracy as high as 86.71%. - This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.