VTLAS

The Virtual Transportation Laboratory for Autonomous Systems (VTLAS) aims to provide a stochastic simulation platform capable of thoroughly evaluating the capabilities of Autonomous Vehicles' (AVs) Automated Driving Systems (ADS), especially under adverse winter driving conditions. The motivation behind this research is to build a reliable tool that can accurately model stochastic vehicle behaviors, study vehicle dynamics, and predict potential AV safety risks when faced with icy or snowy road conditions. This is crucial for ensuring the safety and reliability of AVs before widespread implementation.

Enter VTLAS 

PIMLAP

The Physics-Informed Machine Learning Application Platform (PIMLAP) is designed to support research and education on the application of PIML in transportation engineering.  PIMLAP provides users with a foundational understanding of PIML, including its theoretical foundation and practical advantages in terms of model interpretability, robustness, and generalizability. The platform features illustrative application cases, such as traffic flow modeling, accompanied by tutorials to facilitate reproducibility and learning. Beyond instructional content, PIMLAP functions as a collaborative space for sharing source code, benchmarking models, and engaging in research discussions, helping to foster a vibrant and growing community of transportation researchers and practitioners. 

Enter PIMLAP