Blind Spot in DUI Countermeasures - Dependent Drivers Are Out of Traffic Safety System Measures

Zámečník, Petr; Havlíčková, Darina; Gregorovič, Adam; Trepáčová, Martina

doi:10.1007/978-3-030-80012-3_7

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Traffic-risk incidents create dangerous situations that may lead to a collision between the ego-vehicle and other moving objects without any notice [15]. We suppose an incident occurs unexpectedly, such as a sudden collision or a pedestrian entering the road.…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Online Traffic-Risk Incident Prediction in First-Person Dash Camera Videos

Pradana

2023

BDCC

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Predicting traffic risk incidents in first-person helps to ensure a safety reaction can occur before the incident happens for a wide range of driving scenarios and conditions. One challenge to building advanced driver assistance systems is to create an early warning system for the driver to react safely and accurately while perceiving the diversity of traffic-risk predictions in real-world applications. In this paper, we aim to bridge the gap by investigating two key research questions regarding the driver’s current status of driving through online videos and the types of other moving objects that lead to dangerous situations. To address these problems, we proposed an end-to-end two-stage architecture: in the first stage, unsupervised learning is applied to collect all suspicious events on actual driving; in the second stage, supervised learning is used to classify all suspicious event results from the first stage to a common event type. To enrich the classification type, the metadata from the result of the first stage is sent to the second stage to handle the data limitation while training our classification model. Through the online situation, our method runs 9.60 fps on average with 1.44 fps on standard deviation. Our quantitative evaluation shows that our method reaches 81.87% and 73.43% for the average F1-score on labeled data of CST-S3D and real driving datasets, respectively. Furthermore, the proposed method has the potential to assist distribution companies in evaluating the driving performance of their driver by automatically monitoring near-miss events and analyzing driving patterns for training programs to reduce future accidents.

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Section: Introductionmentioning

confidence: 99%

An End-to-End Online Traffic-Risk Incident Prediction in First-Person Dash Camera Videos

Pradana

2023

BDCC

View full text Add to dashboard Cite

show abstract

Blind Spot in DUI Countermeasures - Dependent Drivers Are Out of Traffic Safety System Measures

Cited by 1 publication

References 6 publications

An End-to-End Online Traffic-Risk Incident Prediction in First-Person Dash Camera Videos

An End-to-End Online Traffic-Risk Incident Prediction in First-Person Dash Camera Videos

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