Driver Lane-Changing Intention Recognition Based on Stacking Ensemble Learning in the Connected Environment: A Driving Simulator Study

The connected environment provides information on surrounding traffic and areas beyond the visual range traffic to improve driving behavior and avoid dangerous incidents. However, due to the novelty of the connected environment, there is a lack of studies on driver lane-changing intention (LCI). Therefore, this study builds a connected lane-changing scenario based on a driving simulator. The differences of the LCI feature parameters are analyzed, and a driver LCI recognition model is developed. The results show that there is a significant difference in vehicle motion parameters, driver operation parameters, and driver’s eye and head movement parameters during the lane-changing left, lane-keeping, and lane-changing right stages. In addition, the length of the intention time window with connected information (6.6 s) is longer than that of the non-connected information (4.1 s). A driver LCI recognition model is developed using Stacking ensemble learning and then compared with traditional algorithms. The results show that the accuracy of the LCI recognition model using Stacking ensemble learning is higher than that of the traditional algorithms when it is recognized 0.5 s and 3 s before the lane-changing maneuver, which are 98.24% and 92.23%, respectively. The driver LCI is sent to the macro-traffic system or surrounding vehicles through connected communication, which not only helps the macro-traffic system to coordinate and control the traffic flow but also helps surrounding vehicles plan the motion trajectory in advance. In addition, the accurate recognition of driver LCI is beneficial to the transfer and distribution of the human-machine co-driving system control rights.