An intelligent driver assistance system to anticipate the human traits and the accidents

• Main Authors: RamPrasad Palarapu, 巴若璞
• Other Authors: Huang, Ching-Yao
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/hf2s6k

碩士 === 國立交通大學 === 電機資訊國際學程 === 105 === This thesis proposes a novel design of an intelligent driver warning system that learns each individual trait of the driver both stationary and challenging and use this information to improve the driving habits of a driver. Most of the current warning systems are physics based as they look at the vehicle trajectory, but mainly ignore the abilities as well as the characteristics of the driver. A number of research issues are involved in this work, as it has to improve upon the state of the art, yet not so complicated to use that the average driver would feel comfortable using it. Intelligent driver warning systems can be found in many high-end vehicles on the road today, which will likely rapidly increase as they become standard equipment. However, introducing multiple warning systems into vehicles could potentially add to the complexity of the driving task, and there are many critical human factors, vehicle factors and environmental factors issues that should be considered, such as how they could interact between one factor to another along with the ages and gender, warning alert schemes, system reliabilities, and distractions combine to affect driving performance and situation awareness. In this proposal, I will discuss these issues and describe preliminary results in using different relationship models and prediction algorithm to predict the driver’s features as well as the accidents. In addition, I will also discuss how different features contribute their values to lead to the different behaviors.