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Active Safety Driving with Driver's ...

Hong, Pei-Heng.

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Active Safety Driving with Driver's Intention Identification and Behavior Prediction.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Active Safety Driving with Driver's Intention Identification and Behavior Prediction./
作者:	Hong, Pei-Heng.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	48 p.
附註:	Source: Masters Abstracts International, Volume: 81-12.
Contained By:	Masters Abstracts International81-12.
標題:	Computer science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27962468
ISBN:	9781083591937

Active Safety Driving with Driver's Intention Identification and Behavior Prediction.
Hong, Pei-Heng.

Active Safety Driving with Driver's Intention Identification and Behavior Prediction. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 48 p.

Source: Masters Abstracts International, Volume: 81-12.

Thesis (M.S.)--Texas A&M University - Commerce, 2020.

This item must not be sold to any third party vendors.

Active safety technologies and driving have become increasingly important and being used in our modern life. They can efficiently help drivers prevent and minimize the effects of accidents by interpreting sensor information to perceive the vehicle's surrounding environment. Sensor information is first processed by vehicles' central embedded system (also called active safety systems) and then used to determine the level of intervention for safe driving.However, most research is focused on either finding optimal methods for fusing and interpreting sensor information without consideration of drivers' continuous attention and behavior, or with improving the configurations of active safety systems with high-cost components such as LIDAR, night-vision cameras, or radar sensor arrays. We find that the effectiveness of active safety technologies and systems is greatly affected by drivers' attention and behaviors. In this thesis, drivers' attention and behaviors are characterized and jointly studied with a novel framework based on Convolutional Neural Networks. The framework is designed for general purposes with off-the-shelf dashboard cameras to assess and predict dangerous behaviors. An identification process is developed to carefully combine driver behaviors, attention, images from the vehicle with corresponding throttle and steering angle. We then identify driver behavior patterns, and construct a machine learning model for image processing and danger assessment. Finally, a model is generated to evaluate the performance of existing and proposed methods with high fidelity before deploying on vehicles in the real world. The stable performance of our framework shows the effectiveness and advantages of driver intention identification and behavior prediction for avoiding fatal collisions and eliminate the probability of potential unsafe driving behavior.

ISBN: 9781083591937Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Active safety driving

Active Safety Driving with Driver's Intention Identification and Behavior Prediction.
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520 $a Active safety technologies and driving have become increasingly important and being used in our modern life. They can efficiently help drivers prevent and minimize the effects of accidents by interpreting sensor information to perceive the vehicle's surrounding environment. Sensor information is first processed by vehicles' central embedded system (also called active safety systems) and then used to determine the level of intervention for safe driving.However, most research is focused on either finding optimal methods for fusing and interpreting sensor information without consideration of drivers' continuous attention and behavior, or with improving the configurations of active safety systems with high-cost components such as LIDAR, night-vision cameras, or radar sensor arrays. We find that the effectiveness of active safety technologies and systems is greatly affected by drivers' attention and behaviors. In this thesis, drivers' attention and behaviors are characterized and jointly studied with a novel framework based on Convolutional Neural Networks. The framework is designed for general purposes with off-the-shelf dashboard cameras to assess and predict dangerous behaviors. An identification process is developed to carefully combine driver behaviors, attention, images from the vehicle with corresponding throttle and steering angle. We then identify driver behavior patterns, and construct a machine learning model for image processing and danger assessment. Finally, a model is generated to evaluate the performance of existing and proposed methods with high fidelity before deploying on vehicles in the real world. The stable performance of our framework shows the effectiveness and advantages of driver intention identification and behavior prediction for avoiding fatal collisions and eliminate the probability of potential unsafe driving behavior.
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