Automated Vehicles (AVs) are attracting attention as a safer mobility option thanks to the recent advancement of various sensing technologies that realize a much quicker Perception–Reaction Time than Human-Driven Vehicles (HVs). However, AVs are not entirely free from the risk of accidents, and we currently lack a systematic and reliable method to improve AV safety functions. The manual composition of accident scenarios does not scale. Simulation-based methods do not fully cover the peculiar AV accident patterns that can occur in the real world. Artificial Intelligence (AI) techniques are employed to identify the moments of accidents from ego-vehicle videos. However, most AI-based approaches fall short in accounting for the probable causes of the accidents. Neither of these AI-driven methods offer details for authoring accident scenarios used for AV safety testing. In this paper, we present a customized Vision Transformer (named ViT-TA) that accurately classifies the critical situations around traffic accidents and automatically points out the objects as probable causes based on an Attention map. Using 24,740 frames from Dashcam Accident Dataset (DAD) as training data, ViT-TA detected critical moments at Time-To-Collision (TTC) ≤ 1 s with 34.92 higher accuracy than the state-of-the-art approach. ViT-TA’s Attention map highlighting the critical objects helped us understand how the situations unfold to put the hypothetical ego vehicles with AV functions at risk. Based on the ViT-TA-assisted interpretation, we systematized the composition of Functional scenarios conceptualized by the PEGASUS project for describing a high-level plan to improve AVs’ capability of evading critical situations. We propose a novel framework for automatically deriving Logical and Concrete scenarios specified with 6-Layer situational variables defined by the PEGASUS project. We believe our work is vital towards systematically generating highly reliable and trustworthy safety improvement plans for AVs in a scalable manner.

Automated Vehicles (AVs) are expected to dramatically reduce traffic accidents that have occurred when using human driving vehicles (HVs). However, despite the rapid development of AVs, accidents involving AVs can occur even in ideal situations. Therefore, in order to enhance their safety, “preventive design” for accidents is continuously required. Accordingly, the “preventive design” that prevents accidents in advance is continuously required to enhance the safety of AVs. Specially, black ice with characteristics that are difficult to identify with the naked eye—the main cause of major accidents in winter vehicles—is expected to cause serious injuries in the era of AVs, and measures are needed to prevent them. Therefore, this study presents a Convolutional Neural Network (CNN)-based black ice detection plan to prevent traffic accidents of AVs caused by black ice. Due to the characteristic of black ice that is formed only in a certain environment, we augmented image data and learned road environment images. Tests showed that the proposed CNN model detected black ice with 96% accuracy and reproducibility. It is expected that the CNN model for black ice detection proposed in this study will contribute to improving the safety of AVs and prevent black ice accidents in advance.

As ridesharing services (including taxi) are often run by private companies, profitability is the top priority in operation. This leads to an increase in the driver’s refusal to take passengers to areas with low demand where they will have difficulties finding subsequent passengers, causing problems such as an extended waiting time when hailing a vehicle for passengers bound for these regions. The study used Seoul’s taxi data to find appropriate surge rates of ridesharing services between 10:00 p.m. and 4:00 a.m. by region using a reinforcement learning algorithm to resolve this problem during the worst time period. In reinforcement learning, the outcome of centrality analysis was applied as a weight affecting drivers’ destination choice probability. Furthermore, the reward function used in the learning was adjusted according to whether the passenger waiting time value was applied or not. The profit was used for reward value. By using a negative reward for the passenger waiting time, the study was able to identify a more appropriate surge level. Across the region, the surge averaged a value of 1.6. To be more specific, those located on the outskirts of the city and in residential areas showed a higher surge, while central areas had a lower surge. Due to this different surge, a driver’s refusal to take passengers can be lessened and the passenger waiting time can be shortened. The supply of ridesharing services in low-demand regions can be increased by as much as 7.5%, allowing regional equity problems related to ridesharing services in Seoul to be reduced to a greater extent.

As new mobility called automated vehicles (AVs) appears on the road, positive effects are expected, but in fact, unexpected adverse effects may arise due to the mixed traffic situation with human-driven vehicles (HVs). Prior to the commercialization of AVs, a preliminary review and preventive measures are required, and among them, the interaction between the existing vehicle and the new mobility and the interaction with the infrastructure must be considered. Therefore, we propose (i) the positive–negative effect of introducing AVs in a mixed traffic situation and (ii) the optimal operation plan for the dedicated lane for AVs. First, the effect of introducing AVs considering the interaction between vehicles in the mixed traffic situation showed mostly positive such as speed increase, delay time reduction, and capacity increase. However, in a 75% Market Penetration Rate (MPR) environment of all levels of Service (LOS), the effect was diminished compared to the previous MPR. This is contemplated to be the result of a conflict caused by the operation of some HVs (including heavy vehicles) behavior as obstacles in the situation where most of the vehicles on the road are AVs. Based on the previous result, we deployed the dedicated lane to resolve the negative effect in the 75% MPR environment and proposed an optimal operation strategy for the AVs dedicated lane from the perspective of operational efficiency for a more feasible operation. Given the 75% MPR, the Mixed-Use operation strategy of High-Occupancy Vehicles (HOV) and AVs is ascertained as the most suitable operation strategy. This implies that even in the era of AVs, the influence of other vehicles (e.g., heavy vehicles, other mobility) must be considered. This study is significant by considering the negative effects of the introduction of AVs and presenting an optimal operation strategy for dedicated lanes, and it can expect to be used as a new strategy as part of the Free/Expressway Traffic Management System (FTMS) applicable in the era of autonomous driving.

As automated vehicles have been considered one of the important trends in intelligent transportation systems, various research is being conducted to enhance their safety. In particular, the importance of technologies for the design of preventive automated driving systems, such as detection of surrounding objects and estimation of distance between vehicles. Object detection is mainly performed through cameras and LiDAR, but due to the cost and limits of LiDAR’s recognition distance, the need to improve Camera recognition technique, which is relatively convenient for commercialization, is increasing. This study learned convolutional neural network (CNN)-based faster regions with CNN (Faster R-CNN) and You Only Look Once (YOLO) V2 to improve the recognition techniques of vehicle-mounted monocular cameras for the design of preventive automated driving systems, recognizing surrounding vehicles in black box highway driving videos and estimating distances from surrounding vehicles through more suitable models for automated driving systems. Moreover, we learned the PASCAL visual object classes (VOC) dataset for model comparison. Faster R-CNN showed similar accuracy, with a mean average precision (mAP) of 76.4 to YOLO with a mAP of 78.6, but with a Frame Per Second (FPS) of 5, showing slower processing speed than YOLO V2 with an FPS of 40, and a Faster R-CNN, which we had difficulty detecting. As a result, YOLO V2, which shows better performance in accuracy and processing speed, was determined to be a more suitable model for automated driving systems, further progressing in estimating the distance between vehicles. For distance estimation, we conducted coordinate value conversion through camera calibration and perspective transform, set the threshold to 0.7, and performed object detection and distance estimation, showing more than 80% accuracy for near-distance vehicles. Through this study, it is believed that it will be able to help prevent accidents in automated vehicles, and it is expected that additional research will provide various accident prevention alternatives such as calculating and securing appropriate safety distances, depending on the vehicle types.