Effect of environmental and eye-tracking information: An Artificial Neural Network-based state machine approach for human driver intention recognition

Driving intention recognition is an important aspect of Advanced Driving Assistance Systems (ADAS) for giving drivers suggestions to maneuver safely. The intention recognition algorithms in ADAS are often developed using Machine Learning-based models. The model's input, such as environmental (ENV) and eye-tracking (ET) features affect the model's recognition performance. In this contribution, an Artificial Neural Network-based state machine is used for lane changing intention recognition. Three lane changing behaviors are considered, left/right lane change and lane keeping. Here, data consisting of ENV and ET information are collected using a driving simulator and eye-tracker. The aim is to investigate the effect of different feature types on the model's intention recognition performance. First, a 10-cross validation is performed to evaluate the model's performance, using only ENV and both ENV and ET features. The validation results show that the model with only ENV features performs better with respect to different metrics. Thus in the test, only ENV features are used to evaluate the performance. Accuracy values of higher than 80 % are achieved. Furthermore, the recognition performance of the model is compared with other Machine Learning models. The approach introduced outperforms other models in most metrics.