This study proposes a Big Data technique-based crash scene re-construction method in order to provide a systematic and effective way of developing critical traffic safety test scenarios for automated vehicles. It is widely understood that crashes occur through conflicts between road users and unsafe traffic conditions. Crash data are thus a useful resource to capture unsafe traffic conditions. Nevertheless, in practice many of the automated vehicle traffic test scenarios have been developed based on engineer’s judgment or aggregated crash statistics and studies, because crash data are too abundant to be comprehensively investigated. The proposed Big Data technique-based method provides the following innovations in developing automated vehicle test scenarios: abundant crash data can be investigated at once and in a short period of time using a text-weight analysis, which is one of the representative Big Data analytics; a comprehensive set of automated vehicle test scenarios can be developed by taking all significant words that frequently appear in crash descriptions into consideration; and the method can even be flexible in terms of capturing significant words by excluding or including specific words and setting a threshold of the word frequency. This proposed Big Data technique-based method is validated in comparison with the resulting test scenarios from a manual investigation of crash data, and it was found that 14 of a total of 18 scenarios correspond to the scenarios from manual investigation and the other four scenarios are additionally derived by the proposed approach. With these innovations in mind, the Big Data technique-based method proposed in this study is not only a systematic and practical approach but also leads to the development of a comprehensive set of automated vehicle test scenarios, by comprehensively taking into consideration significant words of crash descriptions, which should be carefully considered in developing automated vehicle test scenarios.

Lane-Change (LC) maneuvers are deemed to jeopardize traffic safety, mobility, and sustainability. Cooperative Lane-Change (CLC) solves this problem by accelerating the LC process. This research proposes an optimal control based CLC controller. It bears the following features: i) with the faster completion of a CLC maneuver by making space and changing lane at the same time; ii) with less speed oscillation perturbations on traffic. To make space and change lane at the same time, a formulation method in the relative spatial domain is proposed. In this method, the conventional longitudinal and lateral coupled nonlinear avoidance constraints are linearized into a relative longitudinal distance fixed lateral-only avoidance constraint. Evaluations indicate that the proposed method enhances the completion efficiency of CLC by 15.67%, reduce traffic emission by 0.24%, reduce fuel consumption by 0.36%, and improve traffic stability by 47.9%.

This study investigates the probability of visibility limitation of autonomous driving system sensors under various road geometry conditions. This limited visibility can be a concern for the safety of the transportation system, while it is believed that the reliable autonomous driving system will improve traffic safety based on the enhanced detection and recognition capability of autonomous vehicle sensors. So, this study is conducted with the assumption that inadequate road geometry specifications can negatively affect the reliability of the sensor performance and ultimately increase autonomous vehicle crash risks. First, the study analyzes the forward detection range of an autonomous vehicle’s light detection system and radar sensors, which are affected by road curvature and slope. Second, the forward detection range of an AV is analyzed according to the actual road geometry data of expressways, national highways, and urban roads. The findings of this study emphasizes that even though autonomous vehicle manufacturers claim that their vehicles are equipped with specific automation levels allowing these vehicles to be safely driven on a particular road category, there are certain road sections that will be difficult for autonomous vehicles to traverse because of poor road geometry. Furthermore, by estimating forward detection range relative to actual road geometry data, it is demonstrated that the performance requirement level of autonomous driving systems increases when road geometries are less complex.

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.