# Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior

arXiv: https://arxiv.org/abs/2211.01294 · HTML: https://mlsanity.com/paper/2211.01294/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [An LSTM Network for Highway Trajectory Prediction](https://mlsanity.com/paper/1801.07962/) (2018)
- [Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture](https://mlsanity.com/paper/1802.06338/) (2018)
- [Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning](https://mlsanity.com/paper/2010.03118/) (2020)
- [Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture](https://mlsanity.com/paper/1601.00740/) (2016)
- [Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning](https://mlsanity.com/paper/1809.02926/) (2018)
- [Self-Driving like a Human driver instead of a Robocar: Personalized comfortable driving experience for autonomous vehicles](https://mlsanity.com/paper/2001.03908/) (2020)
- [Digital Twin-Assisted Cooperative Driving at Non-Signalized Intersections](https://mlsanity.com/paper/2105.01357/) (2021)
- [A Learning-Based Approach for Lane Departure Warning Systems with a Personalized Driver Model](https://mlsanity.com/paper/1702.01228/) (2017)
- [Vision-Cloud Data Fusion for ADAS: A Lane Change Prediction Case Study](https://mlsanity.com/paper/2112.04042/) (2021)
- [Attention-based Lane Change Prediction](https://mlsanity.com/paper/1903.01246/) (2019)
- [Long-Term Prediction of Lane Change Maneuver Through a Multilayer Perceptron](https://mlsanity.com/paper/2006.12769/) (2020)

## Built on by (most cited first, 13 of 13)

- [Personalized Autonomous Driving with Large Language Models: Field Experiments](https://mlsanity.com/paper/2312.09397/) (2023)
- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [StyleDrive: Towards Driving-Style Aware Benchmarking of End-To-End Autonomous Driving](https://mlsanity.com/paper/2506.23982/) (2025)
- [On-Board Vision-Language Models for Personalized Autonomous Vehicle Motion Control: System Design and Real-World Validation](https://mlsanity.com/paper/2411.11913/) (2024)
- [CEMFormer: Learning to Predict Driver Intentions from In-Cabin and External Cameras via Spatial-Temporal Transformers](https://mlsanity.com/paper/2305.07840/) (2023)
- [PDB: Not All Drivers Are the Same -- A Personalized Dataset for Understanding Driving Behavior](https://mlsanity.com/paper/2503.06477/) (2025)
- [Real-time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control](https://mlsanity.com/paper/2309.05115/) (2023)
- [Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks](https://mlsanity.com/paper/2308.07439/) (2023)
- [Investigating Personalized Driving Behaviors in Dilemma Zones: Analysis and Prediction of Stop-or-Go Decisions](https://mlsanity.com/paper/2405.03873/) (2024)
- [Can Users Specify Driving Speed? Bench2Drive-Speed: Benchmark and Baselines for Desired-Speed Conditioned Autonomous Driving](https://mlsanity.com/paper/2603.25672/) (2026)
- [Driving Like Yourself: A Benchmark for Closed-Loop Personalized End-to-End Autonomous Driving](https://mlsanity.com/paper/2602.18757/) (2026)
- [Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network](https://mlsanity.com/paper/2407.07575/) (2024)
- [Scene-Aware Conversational ADAS with Generative AI for Real-Time Driver Assistance](https://mlsanity.com/paper/2507.10500/) (2025)

## Abstract (arXiv)

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.