An Artificial Intelligence-Based Vehicular System Simulator

2. Background Literature

Some research studies on cognitive vehicular systems are briefly summarized as follows. Xie & Xu (2015) provided an overview of the algorithms and technologies useful for extracting static properties of vehicles from videos so as to support criminal and traffic violations. With the rapid growth of the video surveillance technology, a large database of video and images has been created. The database can become an important resource for police in performing traffic events analysis: crucial information such as speed and license plate of vehicles can be extracted and examined. Yoshizawa & Iwasaki (2015) examined a type of driving behavior, called “aimless driving”, which is responsible for a large number of fatal traffic accidents in Japan. The study investigates this “aimless driving” behavior by giving subjects non-visual secondary tasks of four difficulty levels while they watch pedestrians and their responses to objects in the road on which they travel are measured. The results indicated that even non-visual tasks influence eye movement and the subjects were not able to react well to objects on the road. Xu et al. (2014) studied the use of semantic technologies for annotating videos so as to enhance comprehensibility of the videos. Their work proposed a video annotation platform, which supports users by providing a search interface of annotated video resources. Some preliminary results indicated that applying semantic technologies for annotation of videos can enhance reusability, scalability, and extensibility of the video resource and support increased adoption of this resource for traffic events analysis.