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你是如何驾驶的：用于驾驶行为分析的同伴和时间感知表征学习

You Are How You Drive: Peer and Temporal-Aware Representation Learning for Driving Behavior Analysis
课程网址: http://videolectures.net/kdd2018_zhang_drive_representation/  
主讲教师: Jumbo Zhang
开课单位: 佛罗里达州立大学
开课时间: 2018-11-23
课程语种: 英语
中文简介:
驾驶是一项复杂的活动，需要多层次的熟练操作（例如，加速、制动、转弯）。分析驾驶行为可以帮助我们评估驾驶员的表现，改善交通安全，最终促进智能和弹性交通系统的发展。虽然已经为分析驾驶行为做出了一些努力，但通过共同探索驾驶行为的同伴和时间依赖性，可以通过表示学习来改进现有方法。为此，在本文中，我们开发了基于对等和时间感知表示学习的框架（PTARL），用于使用GPS轨迹数据进行驾驶行为分析。具体来说，我们首先从GPS轨迹中检测每个驾驶员的驾驶操作和状态。然后，我们从驾驶状态序列中导出一系列多视图驾驶状态转换图，以表征驾驶员随时间变化的驾驶行为。此外，我们开发了一种对等和时间感知表示学习方法，以从驱动状态转换图中学习一系列时变但关系矢量化的表示。所提出的方法可以在统一的优化框架中同时对图-图对等依赖性和当前过去的时间依赖性进行建模。此外，我们还为优化问题提供了有效的解决方案。此外，我们利用学习到的驾驶行为表示来对驾驶表现进行评分并检测危险区域。最后，使用大轨迹数据的大量实验结果表明，所提出的驾驶行为分析方法的性能得到了提高。
课程简介: Driving is a complex activity that requires multi-level skilled operations (e.g., acceleration, braking, turning). Analyzing driving behavior can help us assess driver performances, improve traffic safety, and, ultimately, promote the development of intelligent and resilient transportation systems. While some efforts have been made for analyzing driving behavior, existing methods can be improved via representation learning by jointly exploring the peer and temporal dependencies of driving behavior. To that end, in this paper, we develop a Peer and Temporal-Aware Representation Learning based framework (PTARL) for driving behavior analysis with GPS trajectory data. Specifically, we first detect the driving operations and states of each driver from GPS traces. Then, we derive a sequence of multi-view driving state transition graphs from the driving state sequences, in order to characterize a driver’s driving behavior that varies over time. In addition, we develop a peer and temporal-aware representation learning method to learn a sequence of time-varying yet relational vectorized representations from the driving state transition graphs. The proposed method can simultaneously model both the graph-graph peer dependency and the current-past temporal dependency in a unified optimization framework. Also, we provide effective solutions for the optimization problem. Moreover, we exploit the learned representations of driving behavior to score driving performances and detect dangerous regions. Finally, extensive experimental results with big trajectory data demonstrate the enhanced performance of the proposed method for driving behavior analysis.
关 键 词: 熟练操作; 共同探索; 状态转换图
课程来源: 视频讲座网
数据采集: 2022-12-21：chenjy
最后编审: 2023-05-11：chenjy
阅读次数: 21

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