论文标题
舰队学习体系结构，用于在挑战外部条件下增强行为预测
A Fleet Learning Architecture for Enhanced Behavior Predictions during Challenging External Conditions
论文作者
论文摘要
今天，驾驶员援助系统已经有助于使日常交通更加舒适，更安全。但是，仍然有一些情况很少见，但很难同时处理。为了应对这些情况并弥合距离完全自动化驾驶的差距，不仅有必要收集大量数据，而且需要收集正确的数据。这些数据可用于通过机器学习和仿真管道来开发和验证系统。沿着这一行，本文介绍了基于车队学习的体系结构，该体系结构可以持续改进系统，以预测周围交通参与者的运动。此外，提出的架构适用于测试工具，以证明系统的基本可行性。最后，结果表明，该系统收集有意义的数据，这有助于改善基本预测系统。
Already today, driver assistance systems help to make daily traffic more comfortable and safer. However, there are still situations that are quite rare but are hard to handle at the same time. In order to cope with these situations and to bridge the gap towards fully automated driving, it becomes necessary to not only collect enormous amounts of data but rather the right ones. This data can be used to develop and validate the systems through machine learning and simulation pipelines. Along this line this paper presents a fleet learning-based architecture that enables continuous improvements of systems predicting the movement of surrounding traffic participants. Moreover, the presented architecture is applied to a testing vehicle in order to prove the fundamental feasibility of the system. Finally, it is shown that the system collects meaningful data which are helpful to improve the underlying prediction systems.
