This paper presents an ontology-based driving decision making system, which can promptly make safety decisions in real-world driving. Analyzing sensor data for improving autonomous driving safety has become one of the most promising issues in the autonomous vehicles research field. However, representing the sensor data in a machine understandable format for further knowledge processing still remains a challenging problem. In this paper, we introduce ontologies designed for autonomous vehicles and ontology-based knowledge base, which are used for representing knowledge of maps, driving paths, and perceived driving environments. Advanced Driver Assistance Systems (ADAS) are developed to improve safety of autonomous vehicles by accessing to the ontology-based knowledge base. The ontologies can be reused and extended for constructing knowledge base for autonomous vehicles as well as for implementing different types of ADAS such as decision making system.

Energy conservation is one of the hot topics within the domain of traffic problems. It is well known that shortening the distance between vehicles reduces the aerodynamic drag of the lagging (or following) vehicle and leads to energy savings, which benefits the drivers. Recently, systems have been developed in which trucks or vehicles travel in a platoon with reduced headway from the preceding vehicle by using automated driving or driver assistance systems. The objective of the present study is to investigate how human factors, such as driving style, a driver's condition, or a driver's personal characteristics, influence the decision of a driver to close the gap with a preceding vehicle and obtain the benefit of aerodynamic drag reduction. We developed a realistic experimental paradigm for investigating the relationship between distance and several factors including the driver's personal characteristics and the size of preceding vehicle. Our experimental setup made use of real vehicles on a test track, as opposed to a vehicle simulator. We examined behavior of subjects that drove the following vehicle as well as subjects that sat in the passenger seat in the following vehicle. The experimental results demonstrate that all subjects attempted to reduce the distance to the preceding vehicle in order to gain the benefit. Based on the experimental and questionnaire results, we conclude that there are relationships between the category of subjects and subject's following distances.

This paper proposes an improved color barycenter model (CBM) and its separation for automatic road sign (RS) detection. The previous version of CBM can find out the colors of RS, but the accuracy is not high enough for separating the magenta and blue regions and the influence of number with the same color are not considered. In this paper, the improved CBM expands the barycenter distribution to cylindrical coordinate system (CCS) and takes the number of colors at each position into account for clustering. Under this distribution, the color information can be represented more clearly for analyzing. Then aim to the characteristic of barycenter distribution in CBM (CBM-BD), a constrained clustering method is presented to cluster the CBM-BD in CCS. Although the proposed clustering method looks like conventional K-means in some part, it can solve some limitations of K-means in our research. The experimental results show that the proposed method is able to detect RS with high robustness.

This paper presents a novel color descriptor based on the proposed Color Barycenter Hexagon (CBH) model for automatic Road-Sign (RS) detection. In the visual Driver Assistance System (DAS), RS detection is one of the most important factors. The system provides drivers with important information on driving safety. Different color combinations of RS indicate different functionalities; hence a robust color detector should be designed to address color changes in natural surroundings. The CBH model is constructed with barycenter distribution in the created color triangle, which represents RS colors in a more compact way. For detecting RS, the CBH model is used to segment color information at the initial step. Furthermore, a judgment process is applied to verify each RS candidate through the size, aspect ratio, and color ratio. Experimental results show that the proposed method is able to detect RS with robust, accurate performance and is invariant to light and scale in more complex surroundings.