Transfer Learning Study of Motion Transformer-based Trajectory Predictions

arXiv:2404.08271v1 Announce Type: new
Abstract: Trajectory planning in autonomous driving is highly dependent on predicting the emergent behavior of other road users. Learning-based methods are currently showing impressive results in simulation-based challenges, with transformer-based architectures technologically leading the way. Ultimately, however, predictions are needed in the real world. In addition to the shifts from simulation to the real world, many vehicle- and country-specific shifts, i.e. differences in sensor systems, fusion and perception algorithms as well as traffic rules and laws, …

abstract architectures arxiv autonomous autonomous driving behavior challenges cs.lg cs.ro driving emergent behavior however planning predictions results simulation study the way trajectory transfer transfer learning transformer type world