Developing a Driver Alert and Alarm System for High Quality Driving Supports

Abstract—Prevention and vigilance plays a key role in preventing threatening risks and potential dangers when one is driving. In other to achieve the goal of averting danger when driving, people need valid and up to date information about: driving behavior, driving environment and vehicle status. There are a lot of factors that needs to be considered in order to keep a driver safe. Drunk driving, blind spots, bad driving habits, fatigue driving, and vehicle status are all factors that needs to be taken into account if safety is to be improved. Driving comfortability greatly depends on vehicle’s status and driver’s behavior. To evaluate the comfort level for a particular ride, we developed a number of formulas for evaluating various variable like vibration which has their base from the ISO. Rules for detecting these factors like fatigue driving, blind spot collision, etc., were set in this study, each of which has a threshold that shouldn’t be exceeded. In this paper, we introduce an in-car monitoring system for driving safety and provide alert and alarm functions using Android device connected to the car’s on-board diagnostics system(OBD-II) and the in-car module

A Non-Training Approach to Generating High Semantic Titles for Chinese Documents

Due to the abundance of available data, manual title generation may become unfeasible. Traditionally, information retrieval has been applied to perform automatic title generation by exploring and searching for keywords from a document. However, titles generated through such a direct combination approach may not satisfy Chinese grammatical rules, and also may not express the semantic meaning explicitly. Thus, we propose a non-learning approach based on conceptual schema to generating titles automatically through sentence modification and recombination. Experimental results prove that our model can satisfy the automatic title generation requirements