Exploring Behavioral Patterns of Lane Change Maneuvers for Human-Like Autonomous Driving

Due to the growing interest in automated driving, a deep understanding on the characteristics of human driving behavior is critical for human-like autonomous vehicles. Among various driving behaviors, lane change is the most important one for vehicle lateral driving safety. This study proposes an unsupervised method to extract and discover the behavioral patterns of lane change maneuvers for the purpose of exploring the composed behavioral patterns during lane change. This method involves two phases: Firstly, the lane change sequences will be segmented into blocks using time-series segmentation algorithms. Three segmentation algorithms were utilized in this study. In the second phase, the segments will be clustered to find the corresponding behavioral pattern of each segment. Two extended latent Dirichlet allocation (LDA) models were adopted to cluster the segments. The combination of different segmentation and clustering algorithms were evaluated and compared by employing entropy and perplexity as the evaluation criteria. Collected lane change data from naturalistic driving were applied to examine its effectiveness. The results show that this method could effectively mine descriptive behavioral patterns from lane change data. This study provides a promising data mining solution to facilitating deep and comprehensive understanding on driver lane change behaviors, which will promote the development of human-like autonomous vehicles.

Automated summary

This study proposes an unsupervised method to extract and discover the behavioral patterns of lane change maneuvers, aiming to explore the composed behavioral patterns during lane changes. The method involves two phases: segmentation of lane change sequences into blocks and clustering of these blocks to find corresponding behavioral patterns. The results show that the method effectively mines descriptive behavioral patterns from lane change data, providing a promising data mining solution for deep understanding of driver lane change behaviors.