Demo: Distracted Driving Detection

被引：2

作者：

Ucar, Seyhan ^{[1
]}

Muralidharan, Haritha ^{[1
]}

Sisbot, E. Akin ^{[1
]}

Oguchi, Kentaro ^{[1
]}

机构：

[1] Toyota Motor North Amer R&D, InfoTech Labs, Mountain View, CA 94043 USA

来源：

2022 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS AND OTHER AFFILIATED EVENTS (PERCOM WORKSHOPS) | 2022年

关键词：

rear-end collisions; distracted driving detection; field trials;

D O I：

10.1109/PerComWorkshops53856.2022.9767514

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Rear-end collisions, which account for over 33% of crashes, are the most frequent type of accidents in the USA. According to reports on road safety, almost all rear-end collisions are due to the distracted driving behavior of other nearby drivers. Ego vehicles should detect such deviated driving behaviors on other cars and warn their drivers to avoid rear-end crashes. In this paper, we demonstrate such a distracted driving detection system through field trials with multiple test vehicles. The ego vehicle observes the distance to preceding and following vehicles and detects distracted driving behavior on follower vehicles. A warning is generated whenever the follower vehicle exhibits distracted driving behavior.

引用

页数：3

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