  Connected and automated vehicles (CAVs) are supposed to share the road with
human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering
the mixed traffic environment is more pragmatic, as the well-planned operation
of CAVs may be interrupted by HDVs. In the circumstance that human behaviors
have significant impacts, CAVs need to understand HDV behaviors to make safe
actions. In this study, we develop a Driver Digital Twin (DDT) for the online
prediction of personalized lane change behavior, allowing CAVs to predict
surrounding vehicles' behaviors with the help of the digital twin technology.
DDT is deployed on a vehicle-edge-cloud architecture, where the cloud server
models the driver behavior for each HDV based on the historical naturalistic
driving data, while the edge server processes the real-time data from each
driver with his/her digital twin on the cloud to predict the lane change
maneuver. The proposed system is first evaluated on a human-in-the-loop
co-simulation platform, and then in a field implementation with three passenger
vehicles connected through the 4G/LTE cellular network. The lane change
intention can be recognized in 6 seconds on average before the vehicle crosses
the lane separation line, and the Mean Euclidean Distance between the predicted
trajectory and GPS ground truth is 1.03 meters within a 4-second prediction
window. Compared to the general model, using a personalized model can improve
prediction accuracy by 27.8%. The demonstration video of the proposed system
can be watched at https://youtu.be/5cbsabgIOdM.
