Estimation of Driver Lane Change Intention Based on the LSTM and Dempster–Shafer Evidence Theory

Abstract

Rapid and correct estimation of driver lane change intention plays an important role in the advanced driver assistance system (ADAS), which could make the driver improve the reliability of the ADAS system and help to decrease driver workload. In this study, a method based on the long short-term memory network (LSTM) and Dempster–Shafer evidence theory is proposed. The model consists of a preliminary decision-making label and a final decision-making label. Driver visual information, head orientation, and vehicle dynamics are collected by preliminary decision-making label. Then, LSTM is used to calculate the initial probability of the driver lane change (left, right, and lane keeping) maneuver intention. The outputs of LSTM are normalized and assigned a basic probability by the Dempster–Shafer evidence theory. The final decision-making label analyzes the information and outputs the probability of each lane change intention and the decision is to identify the driver's current intention. The experimental results show that the accuracy of the model is 90.7% for the intention of changing left and 89.1% for the intention of changing right. The outcome of this work is an essential component for all levels of road vehicle automation.

Innovation of Visual Communication Design of Interactive Packaging for Internet-Famous Food Based on Artificial Intelligence

“…As an uncertain reasoning method, evidence theory has unique advantages in information fusion and has been widely used [26][27][28][29][30][31][32][33][34]. When the evaluation information of the multiple groups supports the proposition that "the innovation of interactive packaging design is the best," when these evidences are fused together, the fusion results should support this proposition to a greater extent.…”