基於變分自動編碼器之異常駕駛行為辨識

ABNORMAL DRIVING BEHAVIOR DETECTION BASED ON VARIATIONAL AUTOENCODER

為提升國道交通秩序，駕駛人行為的控制與管理為其關鍵。現況相關權責機關多透過歷史交通事故資料，或是巨觀車流狀態作為國道交通管理之依據。然而受限資料細緻度難以瞭解微觀駕駛行為，交通安全秩序提升管理仍有所不足。相關研究多使用車輛動態資訊坪林站1號出口萃取危險駕駛行為，作為事故預測與道路安全改善之主要依據。然而不同駕駛個體之反應與程度亦有所差異，並非所有駕駛面對突發狀況表現皆達到危險駕駛行為之標準。不同於危險駕駛行為具有固定、絕對性標準，異常駕駛行為是一種考量駕駛風格因人而異所產生的動態、相對性標準。本研究使用國道客運車機資料進行駕駛行為分析，並引入異常駕駛行為概念，經變分自動編碼器所偵測之異常駕駛行為，其後與歷史交通事故資料進行比對，探討異常駕駛行為與交通事故之關聯。本研究結果可於事故發生以前，透過網羅更多潛在風險駕駛行為，達到積極道路安全管理。

To improve the highway safety, the regulation and management of driving behaviors is one of the most critical issues. The highway safety management policy which made by the concerned departments usually depends on the historical traffic crash events or macro traffic flow. However, it is hard to have a depth knowledge of microscopic driving behaviors due to the limited data granularity, which is insufficient to achieve the safety regulation improvement. Related studies usually take near crash events extracted from the driving behavior data as the main input of crash prediction and safety improvement. Nevertheless, the driving behaviors varies from drivers, not all drivers react dangerous while emergency. Different from near crash event which owns static and fixed standard, the standard of abnormal driving behavior is dynamic and comparatively. This research uses Variational Autoencoder to detect the abnormal driving behaviors from the data of highway bus and explores the relations between the historical traffic crashes with the comparison. This research can discover the potential risk before crash, which makes highway safer actively.