Understanding Lane Change Behavior Under Dynamic Driving Environment based on Real-world Traffic Dataset

Lane change is a common type of driving behaviors in daily traveling, and is an essential functionality for highly automated vehicles. For the design of proper driving behaviors of automated vehicles, it is important to have a comprehensive understanding of the real-world driving behaviors. In this paper, we conduct an investigation of the lane change behaviors by examining vehicle trajectories, headways and spacing gaps between adjacent vehicles in a real-world traffic dataset, NGSIM. In particular, the identification of lane change trajectories is performed, and the distribution of headways and spacing gaps are extracted. Results show that the characteristics of lane changing actions provide new insight and serve as a valuable reference for understanding of driving behaviors in real world situations.