Unity-Vissim Cosimulation environment development for Human-in-the-loop test

• Existing human-in-the-loop (HIL) test systems have limitations in providing a realistic traffic environment.
• Microsimulation(VISSIM) is mainly used for traffic flow analysis because it embeds models that reflect general human driving behaviors(such as car-following and lane change).
• By integrating the two software, the cosimulation environment was built to analyze driving behaviors in realistically reproduced traffic flow situations.

Networking based Multiagent driving simulator platform development

• Single Driving simulator has limitations in accurately reflecting human factors in collision risk situations, as vehicles in the simulation operate based on specific models.
• To overcome the limitations, a network-based multiagent driving simulator platform was developed, enabling multiple participants to engage simultaneously.
• To verify the reliability of the simulator, scenarios such as car following and unsignalized intersection crossing were selected. Real-world coordinates and speed changes were collected and statistically analyzed alongside corresponding data gathered from the simulators.

KAIST Munji campus testbed transplantation to the virtual world

• To conduct a test-based study, the KAIST Munji campus was scanned using a drone.
• Point cloud data was collected and used for 3D modeling.

• Performed autonomous vehicle data replay in the metaverse environment based on the trajectory provided in Prof. Dongseok Kum’s laboratory (VDC lab).

• Pedestrian model control module was embedded into the developed multiagent simulator platform to extend vehicle-human interactions cases.

Traffic volume imputation based on multi-source data collection system

• Traffic detectors collect traffic data to understand the flow of traffic on a city scale, which can help build an efficient traffic management system or deliver useful traffic information to road users for better decision-making.
• However, various types of missing data occur depending on the data management system and unexpected situations on the road. In the short term, we found that the gaps were hourly, but in the long term, they were monthly.
• We propose a deep learning methodology based on a graph neural network that can simultaneously interpolate the missing values of a large amount of traffic detector data installed at the city level.
• By applying an attention mechanism in the graph domain to cooperatively consider the spatial and temporal correlation of two different types of detectors, the model recovers the original traffic data with an error of less than 12 cars/5 minutes, regardless of the missing data rate.

→ The overall multi-source data aggregation process and imputation procedure

A deep spatio-temporal approach in maritime accident prediction
– Predicting the risk of maritime accidents is crucial for improving traffic surveillance and marine safety.

• This study aims at investigating the application of deep learning in both short- and long-term predictions of different types of accident risks associated with small vessels by considering multiple influencing factors.
• The results reveal that although the performance of the proposed deep spatiotemporal ocean accident prediction (DSTOAP) model varies according to grid sizes and time intervals, its accuracy (more than 78%) makes it reliable for predicting accidents.
• Furthermore, although all types of accidents are captured with high accuracy, more than 84% of collision accidents can be predicted accurately.

→ The proposed DSTOAP architecture and prediction results for each grid size