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Connected and automated vehicles (CAVs) are supposed to share the road withhuman-driven vehicles (HDVs) in a foreseeable future. Therefore, consideringthe mixed traffic environment is more pragmatic, as the well-planned operationof CAVs may be interrupted by HDVs. In the circumstance that human behaviorshave significant impacts, CAVs need to understand HDV behaviors to make safeactions. In this study, we develop a Driver Digital Twin (DDT) for the onlineprediction of personalized lane change behavior, allowing CAVs to predictsurrounding vehicles' behaviors with the help of the digital twin technology.DDT is deployed on a vehicle-edge-cloud architecture, where the cloud servermodels the driver behavior for each HDV based on the historical naturalisticdriving data, while the edge server processes the real-time data from eachdriver with his/her digital twin on the cloud to predict the lane changemaneuver. The proposed system is first evaluated on a human-in-the-loopco-simulation platform, and then in a field implementation with three passengervehicles connected through the 4G/LTE cellular network. The lane changeintention can be recognized in 6 seconds on average before the vehicle crossesthe lane separation line, and the Mean Euclidean Distance between the predictedtrajectory and GPS ground truth is 1.03 meters within a 4-second predictionwindow. Compared to the general model, using a personalized model can improveprediction accuracy by 27.8%. The demonstration video of the proposed systemcan be watched at https://youtu.be/5cbsabgIOdM.

Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior