4 research outputs found
Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior
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
Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks
Lane change (LC) is one of the safety-critical manoeuvres in highway driving
according to various road accident records. Thus, reliably predicting such
manoeuvre in advance is critical for the safe and comfortable operation of
automated driving systems. The majority of previous studies rely on detecting a
manoeuvre that has been already started, rather than predicting the manoeuvre
in advance. Furthermore, most of the previous works do not estimate the key
timings of the manoeuvre (e.g., crossing time), which can actually yield more
useful information for the decision making in the ego vehicle. To address these
shortcomings, this paper proposes a novel multi-task model to simultaneously
estimate the likelihood of LC manoeuvres and the time-to-lane-change (TTLC). In
both tasks, an attention-based convolutional neural network (CNN) is used as a
shared feature extractor from a bird's eye view representation of the driving
environment. The spatial attention used in the CNN model improves the feature
extraction process by focusing on the most relevant areas of the surrounding
environment. In addition, two novel curriculum learning schemes are employed to
train the proposed approach. The extensive evaluation and comparative analysis
of the proposed method in existing benchmark datasets show that the proposed
method outperforms state-of-the-art LC prediction models, particularly
considering long-term prediction performance.Comment: 13 pages, 11 figure