| Abstract |
Smart Car is an automobile with an Advanced Driver Assistance System (ADAS) that can provide a more enjoyable driving experience as well as active safety features. The aim of these systems is to ensure road safety and reduce the risk of road accidents. For these purposes, a driver assistance system must be context-sensitive by monitoring the car and its environment in real time besides sensing, analyzing, predicting and reacting according to the following contextual situation: the vehicle state, the driver state, and the physical environment surrounding them. This paper presents a comparative and statistical study of the research published in the last three years on context-based driver assistance systems, depending on variant criteria such as: the context, the algorithms used, and the technologies adopted. We provide an overview of the results of this survey and the algorithms based on, the sensors used, and categories of context studied. We also propose a model that classifies the different contexts and their features based on the literature review. © 2019 Association for Computing Machinery. ACM |
