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A Fleet Learning Architecture for Enhanced Behavior Predictions during Challenging External Conditions
arXiv - CS - Robotics Pub Date : 2020-09-23 , DOI: arxiv-2009.11221 Florian Wirthm\"uller, Marvin Klimke, Julian Schlechtriemen, Jochen Hipp and Manfred Reichert
arXiv - CS - Robotics Pub Date : 2020-09-23 , DOI: arxiv-2009.11221 Florian Wirthm\"uller, Marvin Klimke, Julian Schlechtriemen, Jochen Hipp and Manfred Reichert
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:
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,
更新日期:2020-09-25
中文翻译:
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,