Chapter 5 - Driver Behavior Recognition in Driver Intention Inference Systems

Abstract

Driver decisions and behaviors regarding the surrounding traffic are critical to traffic safety and driver intention inference. Driver intentions are a mental process that cannot be measured directly. However, once the intention is generated, the driver will generate a series of intention-oriented behaviors such as mirror checking and pedal maneuvers to realize the intention. Hence, driver behaviors are the most important features to represent driver intention. Nowadays, it is important for an intelligent vehicle to understand driver behavior and assist in driving tasks according to their status. In this section, a consumer range camera, namely Kinect, is used to monitor drivers and identify driving tasks in a real vehicle. Specifically, seven common tasks performed by multiple drivers during driving are identified in this study. The tasks include normal driving, left, right, and rear mirror checking, mobile phone answering, texting using a mobile phone with one or both hands, and the setup of in-vehicle video devices. The first four tasks are considered safe driving tasks while the other three tasks are regarded as dangerous and distracting tasks. The driver behavior signals collected from the Kinect consist of a color and depth image of the driver inside the vehicle cabin. Additionally, three-dimensional head rotation angles and the upper body (hand and arm at both sides) joint positions are recorded. Then, the importance of these features to behavior recognition is evaluated using random forests and maximal information coefficient methods. Next, a feedforward neural network (FFNN) is used to identify the seven tasks. Finally, the model performance for task recognition is evaluated with different features (body only, head only, and combined). The final detection result for the seven driving tasks among five participants achieved an average of greater than 80% accuracy, and the FFNN tasks detector is proved to be an efficient model that can be implemented for real-time driver distraction and dangerous behavior recognition.