Accurate and Efficient Driving Intention Inference Based on Traffic Environment Information and FES-XGB Framework

Abstract

Reliable driving intention inference is an essential issue in the mixed automation traffic system. To improve traffic safety and efficiency, this study develops an accurate and efficient driving intention inference framework named FES-XGB, which is short for Feature Extraction and Selection based eXtreme Gradient Boosting (XGBoost) algorithm. In contrast with conventional approaches, which only consider motion information of the subject and neighboring vehicles, this study includes a new kind of decision variables into driving intention inference for the first time, i.e., the local and global traffic environment information assumed to be obtained from vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) technology. The high-precision NGSim trajectory dataset is employed to learn the relationship between traffic environment information and driving intentions and evaluate the proposed framework. According to the experiment results, by taking the environment information as additional input, the accuracy of the conventional XGBoost model can increase from 89.42% to 92.86%, indicating the environment information has a close relationship with the driving intention. By employing the proposed FES-XGB framework, the accuracy can be further increased to 94.09%, while the training and online inference cost can be reduced by 94.03% and 65.25% respectively. With the traffic environment information as additional input, the proposed FES-XGB framework can be integrated into advanced driver-assistance systems (ADAS) for a safer and more efficient traffic system.