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.

中文翻译：

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用于在具有挑战性的外部条件期间增强行为预测的舰队学习架构

如今，驾驶员辅助系统已帮助使日常交通更加舒适和安全。但是，仍然存在非常罕见但同时难以处理的情况。为了应对这些情况并缩小与全自动驾驶之间的差距，不仅需要收集大量数据，还需要收集正确的数据。这些数据可用于通过机器学习和模拟管道开发和验证系统。沿着这条路线，本文提出了一种基于车队学习的架构，该架构能够持续改进预测周围交通参与者移动的系统。此外，所提出的架构应用于测试车辆，以证明系统的基本可行性。最后，