As the research and development activities of automated vehicles have been active in recent years, developing test scenarios and methods has become necessary to evaluate and ensure their safety. Based on the current context, this study developed an automated vehicle test scenario derivation methodology using traffic accident data and a natural language processing technique. The natural language processing technique-based test scenario mining methodology generated 16 functional test scenarios for urban arterials and 38 scenarios for intersections in urban areas. The proposed methodology was validated by determining the number of traffic accident records that can be explained by the resulting test scenarios. That is, the resulting test scenarios are valid and represent a matching rate between the test scenarios and the increased number of traffic accident records. The resulting functional scenarios generated by the proposed methodology account for 43.69% and 27.63% of the actual traffic accidents for urban arterial and intersection scenarios, respectively.

This study investigates the impacts of road traffic conditions and driver’s characteristics on the takeover time in automated vehicles using a driving simulator. Automated vehicles are barely expected to maintain their fully automated driving capability at all times based on the current technologies, and the automated vehicle system transfers the vehicle control to a driver when the system can no longer be automatically operated. The takeover time is the duration from when the driver requested the vehicle control transition from the automated vehicle system to when the driver takes full control of the vehicle. This study assumes that the takeover time can vary according to the driver’s characteristics and the road traffic conditions; the assessment is undertaken with various participants having different characteristics in various traffic volume conditions and road geometry conditions. To this end, 25 km of the northbound road section between Osan Interchange and Dongtan Junction on Gyeongbu Expressway in Korea is modeled in the driving simulator; the experiment participants are asked to drive the vehicle and take a response following a certain triggering event in the virtual driving environment. The results showed that the level of service and road curvature do not affect the takeover time itself, but they significantly affect the stabilization time, that is, a duration for a driver to become stable and recover to a normal state. Furthermore, age affected the takeover time, indicating that aged drivers are likely to slowly respond to a certain takeover situation, compared to the younger drivers. With these findings, this study emphasizes the importance of having effective countermeasures and driver interface to monitor drivers in the automated vehicle system; therefore, an early and effective alarm system to alert drivers for the vehicle takeover can secure enough time for stable recovery to manual driving and ultimately to achieve safety during the takeover.

Despite the technological advances in automated driving systems, traffic accidents involving automated vehicles (AVs) continue to occur, raising concerns over the safety and reliability of automated driving. For the smooth commercialization of AVs, it is necessary to systematically assess the driving safety of AVs under the various situations that they potentially face. In this context, these various situations are mostly implemented by using systematically developed scenarios. In accordance with this need, a scenario generation framework for the assessment of the driving safety of AVs is proposed by this study. The proposed framework provides a unified form of assessment with key components for each scenario stage to facilitate systematization and objectivity. The performance of the driving safety assessment scenarios generated within the proposed framework was verified. Traffic accident report data were used for verification, and the usefulness of the proposed framework was confirmed by generating a set of scenarios, ranging from functional scenarios to test cases. The scenario generation framework proposed in this study can be used to provide sustainable scenarios. In addition, from this, it is possible to create assessment scenarios for all road types and various assessment spaces, such as simulations, proving grounds, and real roads.

Owing to the low occurrence of public transport accidents, most existing studies have focused on improving the traffic safety of passenger cars. However, traffic accidents related to public transport should also be investigated for the safety of public transport users, particularly the vulnerable ones. This study analyzed the behavioral factors affecting passenger fall incidents on buses to enhance the safety of public bus passengers. This study considered potential influential factors, such as acceleration, deceleration, braking, and steering maneuvers, calculated using data from digital tachographs installed on the buses. Negative binomial and Poisson lognormal regression models were built using the Bayesian method. The two models yielded similar results. In all cases, deceleration-related behavioral factors (abrupt deceleration and abrupt stop) significantly influenced passenger fall incidents. The findings from this study are significant for establishing effective strategies to reduce fall incidents by providing safety education to bus drivers.

Several research studies are being actively conducted on truck platooning, in which several trucks are organized in one fleet. The main advantages of truck platooning are increased traffic efficiency, improved fuel consumption and driver performance, and enhanced safety. However, these effects may depend on the actual conditions of road geometry and traffic situation. Therefore, this study analyzed the sustainability of truck platooning on an expressway network in terms of road geometry and traffic situation. To select elements affecting truck platooning on an expressway network, an expert survey was conducted. In addition, the weight of each element selected in the survey was determined through an analytic hierarchy process. In this study, the geometric conformity and traffic environment indices for truck platooning were calculated based on the data collected by the congestion zone (Conzone) and the weights. Moreover, the calculated indices were clustered into three different categories through cluster analysis. Based on the cluster analysis result, each Conzone was classified into three types: Type 1, which is suitable for both traffic environment and geometric conformity to operate platooning; Type 2, which has geometrical conditions but requires improvement of traffic environment; and Type 3, which requires geometrical improvement. Finally, the Type 1 Conzones were expanded to expressway sections to consider the network connectivity and truck platoon operability. The obtained results revealed that 14 sections of 9 expressway lines are most applicable for truck platooning.