Adaptation and Personalization in Driver Assistance Systems

Driver-related factors (e.g., driver inattention) are a cause of majority of traffic accidents. To reduce the number of accidents and improve traffic safety a variety of driver assistance systems have been proposed. Today, many of these systems do not adapt recommendations and warning to the particular driver (having his-/her own driving style, reaction time etc.). However, in many cases utilization of personal characteristics and preferences may improve the quality of the driver assistance, besides if a driver's expectations about the functionality provided by the assistance system are not met, it may decrease the trust to the system and lead to turning it off, therefore ignoring its potential utility and influence on increasing the safety. In this paper we review scientific publications in the area of driver assistance systems and a) identify most widely used directions of personalization and adaptation in driver assistance systems, b) identify and describe the most widely used models and methods leveraged for personalization and adaptation, c) identify existing research gaps. The paper may serve as mapping study as well as a reference and a toolset of how to deal with driver variability in driver assistance systems

Safe Intelligent Driver Assistance System in V2X Communication Environments based on IoT

In the modern world, power and speed of cars have increased steadily, as traffic continued to increase. At the same time highway-related fatalities and injuries due to road incidents are constantly growing and safety problems come first. Therefore, the development of Driver Assistance Systems (DAS) has become a major issue. Numerous innovations, systems and technologies have been developed in order to improve road transportation and safety. Modern computer vision algorithms enable cars to understand the road environment with low miss rates. A number of Intelligent Transportation Systems (ITSs), Vehicle Ad-Hoc Networks (VANETs) have been applied in the different cities over the world. Recently, a new global paradigm, known as the Internet of Things (IoT) brings new idea to update the existing solutions. Vehicle-to-Infrastructure communication based on IoT technologies would be a next step in intelligent transportation for the future Internet-of-Vehicles (IoV). The overall purpose of this research was to come up with a scalable IoT solution for driver assistance, which allows to combine safety relevant information for a driver from different types of in-vehicle sensors, in-vehicle DAS, vehicle networks and driver`s gadgets. This study brushed up on the evolution and state-of-the-art of Vehicle Systems. Existing ITSs, VANETs and DASs were evaluated in the research. The study proposed a design approach for the future development of transport systems applying IoT paradigm to the transport safety applications in order to enable driver assistance become part of Internet of Vehicles (IoV). The research proposed the architecture of the Safe Intelligent DAS (SiDAS) based on IoT V2X communications in order to combine different types of data from different available devices and vehicle systems. The research proposed IoT ARM structure for SiDAS, data flow diagrams, protocols. The study proposes several IoT system structures for the vehicle-pedestrian and vehicle-vehicle collision prediction as case studies for the flexible SiDAS framework architecture. The research has demonstrated the significant increase in driver situation awareness by using IoT SiDAS, especially in NLOS conditions. Moreover, the time analysis, taking into account IoT, Cloud, LTE and DSRS latency, has been provided for different collision scenarios, in order to evaluate the overall system latency and ensure applicability for real-time driver emergency notification. Experimental results demonstrate that the proposed SiDAS improves traffic safety