TrafficNet：一个开放的自然主义驾驶场景库
TrafficNet: An Open Naturalistic Driving Scenario Library
摘要：
近年来，在收集自然驾驶数据方面所做的巨大努力导致了公开可用的交通数据集的扩展，这些数据集有可能帮助自动驾驶汽车的开发。然而，我们发现，由于托管这些收集的数据的组织缺乏可用性问题，许多利用这些数据集的尝试在实践中都失败了。例如，从按时间顺序组织的自然驾驶数据中提取与某些关键条件相关联的数据对于没有大数据分析经验的车辆工程师来说可能不方便。为了解决这些公开可用的交通数据集的一般可用性的挑战，我们提出了一个大规模和可扩展的自然驾驶场景库，旨在弥合研究数据集和车辆工程师和研究人员的实际可用信息之间的差距。建议基于Web的驾驶场景数据库预处理大量的原始交通数据收集到一个有组织的基于数据集的时间顺序，通过应用一组分类算法来标记自然的驾驶数据与六个不同的关键驾驶场景。ActuicNet不仅向公众开放场景库，还公开了这些分类方法的源代码，这将促进更复杂和准确的基于ActuicNet的分类算法，以推动智能交通研究。源代码和场景数据库可以在https://github.com/TrafficNet上访问。
英文摘要：
The enormous efforts spent on collecting naturalistic driving data in the recent years has resulted in an expansion of publicly available traffic datasets, which has the potential to assist the development of the self-driving vehicles. However, we found that many of the attempts to utilize these datasets have failed in practice due to a lack of usability concern from the organizations that host these collected data. For example, extracting data associated with certain critical conditions from naturalistic driving data organized in chronological order may not be convenient for a vehicle engineer that doesn't have big data analytics experiences. To address the general usability challenges of these publicly available traffic datasets, we propose TrafficNet, a large-scale and extensible library of naturalistic driving scenarios, aiming at bridging the gap between research datasets and practically usable information for vehicle engineers and researchers. The proposed web-based driving scenario database preprocesses massive raw traffic data collected in chronological order into an organized scenario-based dataset by applying a set of categorization algorithms to label the naturalistic driving data with six different critical driving scenarios. TrafficNet opens not only the scenario library but also the source code of these categorization methods to the public, which will foster more sophisticated and accurate scenario-based categorization algorithms to advance the intelligent transportation research. The source code and the scenario database can be accessed at https://github.com/TrafficNet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
