Fuel Consumption Evaluation of Connected Automated Vehicles Under Rear-End Collisions

Connected automated vehicles (CAV) can increase traffic efficiency, which is considered a critical factor in saving energy and reducing emissions congestion. In this paper, systematic simulations are conducted for three car-following modes, including intelligent driver model (IDM), adaptive cruise control (ACC), cooperative ACC (CACC), congestions caused by rear-end collisions. From the perspectives of lane density, vehicle trajectory speed, fuel consumption under modes compared analysed, respectively. Based on driving accident environment parameters, an XGBoost algorithm-based prediction framework proposed The results show that with IDM CACC mode have ideal performance terms total consumption; besides, flow more stable, speed fluctuation relatively tiny different impact regions, meets desires drivers.