Toward Safe and Smart Mobility: Energy-Aware Deep Learning for Driving Behavior Analysis and Prediction of Connected Vehicles

Abstract

Connected automated driving technologies have shown tremendous improvement in recent years. However, it is still not clear how driving behaviors and energy consumption correlate with each other and to what extent these factors related to connected vehicles can influence the motion prediction performance. The precise recognition of driving behaviors and prediction of the vehicle motion is critical to the driving safety for connected automated vehicles (CAVs). Hence, in this study, an energy-aware driving pattern analysis and motion prediction system are proposed for CAVs using a deep learning-based time-series modeling approach. First, energy-aware longitudinal acceleration and deceleration behaviors and lateral lane-change behaviors are statistically analyzed. Then, a sliding standard deviation (SSD) test is applied to evaluate the smoothness of the trajectory and velocity signals considering different energy consumption levels. An energy-aware personalized joint time-series modeling (PJTSM) approach based on a deep recurrent neural network (RNN) and long short-term memory (LSTM) cell are proposed for accurate motion (trajectory and velocity) prediction of the leading vehicle. Finally, the differences in the prediction performance regarding different energy consumption levels are compared and discussed. It is shown that due to the higher randomness of the driving behaviors, the prediction accuracy for heavy energy users is the lowest among the three categories, which means it is harder to anticipate the driving behaviors of cars exhibiting heavy energy consumption. The personalized estimation of driving behaviors of CAVs will contribute to safer automated driving and transportation systems.