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Transfer Learning Study of Motion Transformer-based Trajectory Predictions

2024-04-12 06:50:32
Lars Ullrich, Alex McMaster, Knut Graichen


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, are on the agenda. Since models that can cover all system setups and design domains at once are not yet foreseeable, model adaptation plays a central role. Therefore, a simulation-based study on transfer learning techniques is conducted on basis of a transformer-based model. Furthermore, the study aims to provide insights into possible trade-offs between computational time and performance to support effective transfers into the real world.

Abstract (translated)

在自动驾驶中,轨迹规划高度依赖于预测其他道路使用者的涌现行为。目前,基于学习的方法在基于模拟的挑战中显示出令人印象深刻的成果,而基于Transformer的架构在技术上领先。然而,最终在现实世界中需要预测。除了从模拟到现实世界的转变之外,还需要考虑许多车辆和国家特定的转变,即传感器系统、融合和感知算法的差异以及交通规则和法律。由于目前还不可能预见到能够覆盖所有系统设置和设计领域的模型,因此模型适应在 central role。因此,基于Transformer的模型进行了一个模拟方面的研究。此外,研究旨在提供关于计算时间和管理性能之间可能出现的权衡的见解,以支持将有效转移至现实世界的有效方法。



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