Automated Vehicles vs. Human Drivers: Modeling Driving Behavior Using Data from Field Experiments
Publication: International Conference on Transportation and Development 2024
ABSTRACT
As automated vehicles (AVs) gradually gain prevalence on public roads, understanding their distinctive driving behavior is crucial for traffic management and planning. This study conducted field experiments using an SAE Level-3/4 AV and collected driving data of AVs and human drivers on public roads using sensors including GPS, radar, camera, and LiDAR. The Wilcoxon rank-sum test is used to identify the difference in the behavior between AVs and human drivers. In addition, logistic regression and Extreme Gradient Boosting (XGBoost) are used to classify AVs and human drivers. Results suggest that there exists a significant difference in driving behavior between AVs and human drivers. Moreover, features including the mean speed and the distance from the vehicle to the detected objects are positively related to the probability of the vehicles being AVs, while the standard deviation of speed and the mean acceleration are negatively associated with it. Furthermore, XGBoost accurately identifies AVs and human drivers using the extracted features with an average area under the curve of 0.92. Results from interpreting results from XGBoost indicate that it performs better when the mean speed is either in the low or high ranges. Moreover, AVs and human drivers are hard to differentiate using the model when the vehicle is too far from other objects. This study underscores the substantial divergence in driving behavior between AVs and human drivers, offering valuable insights for the evaluation of the impact of AVs on traffic conditions.
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International Conference on Transportation and Development 2024
Pages: 560 - 572
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Published online: Jun 13, 2024
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