Lane change strategy analysis and recognition for intelligent driving systems based on random forest

The development of intelligent connected technology provides a platform for multidimensional information interaction and makes the recognition of a specific lane change strategy become a reality. Intelligent driving systems cannot make human-like decisions by only recognizing left or right lane change behaviors; in fact, poor environmental cognitive competence is the main cause of accidents of intelligent driving systems during road tests. Therefore, the recognition of specific lane change strategies can provide support for safety performance improvement. This research aims to identify the different lane change strategies of a subject vehicle with a rear approaching vehicle in the target lane. For the purpose of acquiring different strategies, a total of 42 experienced drivers including 37 males and 5 females participated in the on-road vehicle experiments, and we divided lane change strategies into three categories: mandatory, yielding, and waiting for lane change. The lane change duration time, lane crossing time, and characteristic parameters including distance to lane line, steering wheel angle, relative distance, and relative speed under different lane change strategies were compared and analyzed. Then the random forest classifier was employed to construct a lane change strategy identification model. Results indicated that the maximum value of global recognition accuracy under three input parameters reached 88.78% when the time window was 1.2 s, which was higher than the maximum value of recognition accuracy under four input parameters, but the identification time was 0.1 s later than the model with four input parameters. In addition, the recognition performance of the random forest model, attention-bidirectional long short-term memory (Atten-BiLSTM) model and genetic algorithm-support vector machine (GA-SVM) model was compared. The results demonstrated that the global recognition accuracy and identification time of the proposed model were better than that of GA-SVM model under different input parameters, and the performance was comparable to the Atten-BiLSTM model. The findings provide the basis for the intelligent driving system to make more human-like decisions and improve safety performance.

Automated summary

This study identified different lane change strategies used by vehicles through on-road experiments, including mandatory, yielding, and waiting for lane change strategies, and compared and analyzed different characteristic parameters under these strategies. A random forest classifier was employed to construct a lane change strategy identification model.