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Whereas the development of ADAS seeks to improve driver's overall performance with a particular focus on traffic safety improvement; with it comes the requirement and opportunity to objectively evaluate the effectiveness of the technology in improving safety and overall road traffic efficiency. This study evaluates the effectiveness of ADAS in improving driver's risk perception in near-crash events using a novel metric from risk homeostasis theory (RHT) – Safety Margins, as an indicator. By designing a function that captures the initial and maximum risk-level of drivers when involved in a critical driving event while driving on a field operational test (FOT) route in Wuhan China; a comparison of the risk-level of drivers when ADAS was on as against when ADAS was off can be made, enabling an effective evaluation of the impact of ADAS on driver's risk perception and consequent risk mitigation. The results show that ADAS has a positive impact on the low-risk group and moderate-risk group for all drivers, but a negative impact on the high-risk group for skilled drivers. The impact evaluation is done under varying risk-levels of near-crash events with drivers of different driving experiences. [ABSTRACT FROM AUTHOR]

This paper aims to recognize driving risks in individual vehicles online based on a data-driven methodology. Existing advanced driver assistance systems (ADAS) have difficulties in effectively processing multi-source heterogeneous driving data. Furthermore, parameters adopted for evaluating the driving risk are limited in these systems. The approach of data-driven modeling is investigated in this study for utilizing the accumulation of on-road driving data. A recognition model of driving risk based on belief rule-base (BRB) methodology is built, predicting driving safety as a function of driver characteristics, vehicle state and road environment conditions. The BRB model was calibrated and validated using on-road data from 30 drivers. The test results show that the recognition accuracy of our proposed model can reach about 90% in all situations with three levels (none, medium, large) of driving risks. Furthermore, the proposed simplified model, which provides real-time operation, is implemented in a vehicle driving simulator as a reference for future ADAS and belongs to research on artificial intelligence (AI) in the automotive field. [ABSTRACT FROM AUTHOR]

With the rapid development of Intelligent Transport Systems (ITS), emerging technologies such as Advanced Vehicle Driving Assist Systems, and Connected Vehicles, Intelligent Vehicles introduce new opportunities and challenges into our traffic systems and raise questions regarding the interaction of drivers' cognitive and driving behaviors with these technologies. Meanwhile, development of these technologies makes innovative vehicle data collection, Vehicle to Vehicle communication, and Vehicle to Infrastructure communication possible. [4] introduced a driver behavior influence factor associated with drivers' driving styles comparing to a theoretical curve speed model that only considers the vehicle-road interaction. Driverless vehicles promise to remove the human operator in favor of safer driving, but drivers still need to participate driving tasks in the lower tiers of vehicle automation. [Extracted from the article]

• The effectiveness of ADAS on braking behavior is significant. • Drivers tend to increase braking times and reducing relative speed in the ADAS. • In any road conditions, when exposed in ADAS, the time headway has increased. • The impact of road categories and experience on driving behaviors is significant. • Drivers' acceptance on FCW function is higher than that of LDW. • Drivers' acceptance on expressway and freeway is higher than that on urban road. Various advanced driver assistance systems (ADAS) have been developed to improve drivers' behavior and perceptual ability; however, whether these ADAS have any measurable effect on driving performance needs to be verified by field operational tests. The purpose of this study was to evaluate the effectiveness of ADAS on Chinese drivers as well as any possible influences of roadway type, gender and experience on driving performance, which can be measured by several variables, including longitudinal, lateral and braking behavior. The ADAS used in this study was a Mobileye M630 with forward collision warning (FCW) and lane departure warning (LDW) functions. Thirty-two participants were recruited to drive a vehicle equipped with Mobileye M630. Participants drove the same test route twice. The route consisted of a 12 km urban road, 34 km urban expressway and 45 km freeway as well as a 14 km adaption road. Vehicle dynamics, environmental information and driving operational data was recorded by CAN (Controller Area Network) bus and video cameras. The results show that ADAS significantly affects braking behavior. Braking time increased and relative speed decreased when drivers were exposed to ADAS. The ADAS also significantly affects several longitudinal behaviors, including the longitudinal deceleration and time headway (THW). The occurrence of critically low THW decreased in the experiment. However, there was no significant effect on lateral behavior. Furthermore, driver acceptance of the FCW function was much higher than the LDW function, and acceptance on the expressway and freeway was much higher than on the urban road. The results also reveal the significant influence of road type and experience on driving behaviors. These findings support policy development and technology improvements for future development of ADAS. [ABSTRACT FROM AUTHOR]