Preparation of Pavement Infrastructure for Connected and Autonomous Vehicle Deployment
Studies have been initiated to investigate the potential impact of Connected and Automated Vehicles (CAVs) on transportation infrastructure to support future CAV testing and deployment. However, most existing research only focuses on the different wandering patterns of CAV. To bridge this gap, an apple-to-apple comparison is first performed to systematically reveal the behavioral differences between the human-driven vehicle (HDV) and CAV trajectory patterns for the first time, with the data collected from camera-based Next Generation Simulation (NGSIM) dataset and autonomous driving co-simulation platform, CARLA and SUMO, respectively. A gradient boosting-based ensemble learning model for pavement performance (i.e., International Roughness Index, IRI) prediction is then developed with the input features including three driving pattern features, namely, lateral wandering deviation, longitudinal car-following distance, and driving speed, plus other twenty context variables. A total of 1,707 observations is extracted from the Long-Term Pavement Performance (LTPP) database for model training purposes. The result indicates that the trained model can accurately predict pavement deterioration, and that CAV deteriorates pavement faster than HDV by 8.1% on average. The results of sensitivity analysis show that CAV deployment will create a greater impact on the younger pavements, and the rate of pavement deterioration is found to be stable under light traffic, whereas it will increase under congested traffic.
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| Work Title | Preparation of Pavement Infrastructure for Connected and Autonomous Vehicle Deployment |
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| License | MIT License |
| Work Type | Report |
| Publication Date | August 31, 2024 |
| Deposited | May 24, 2025 |
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