Abstract
Identifying unusual driving behaviors exhibited by drivers during driving is essential for understanding driver behavior and the underlying causes of crashes. Previous studies have primarily approached this problem as a classification task, assuming that naturalistic driving videos come discretized. However, both activity segmentation and classification are required for this task due to the continuous nature of naturalistic driving videos. The current study therefore departs from conventional approaches and introduces a novel methodological framework, DeepSegmenter, that simultaneously performs activity segmentation and classification in a single framework. The proposed framework consists of four major modules namely Data Module, Activity Segmentation Module, Classification Module and Postprocessing Module. Our proposed method won 8th place in the 2023 AI City Challenge, Track 3, with an activity overlap score of 0.5426 on experimental validation data. The experimental results demonstrate the effectiveness, efficiency, and robustness of the proposed system.
Abstract (translated)
识别Driver在驾驶时表现出的异常情况对于理解Driver的行为和事故根本原因至关重要。以往的研究主要将这个问题视为分类任务，假设自然驾驶视频是离散的。然而，由于自然驾驶视频的连续性质，这项任务需要同时进行活动分割和分类。因此，本研究采用了一种全新的方法框架DeepSegmenter，该框架在一个框架内同时完成活动分割和分类。该框架由四个主要模块组成，分别是数据模块、活动分割模块、分类模块和后处理模块。我们提出的方法和在2023年AI城市挑战(Track 3)的第8名比赛中，在实验验证数据上获得了活动重叠得分0.5426。实验结果证明了该方案的有效性、效率和鲁棒性。
URL
https://arxiv.org/abs/2304.08261
