Region Recognition Based on HMM Using Primitive Motion Transitions

Automated Driving Systems (ADSs) are being proposed as a promising technology that will help drivers avoid accidents, as well as reduce driving-related stress. To enhance the driving performance of ADSs, driving log data from human drivers is being used to teach these systems how to behave in different traffic regions. Visual information from the driving log data is an easy method to identify traffic regions. However, due to space constraints or lack of visual sensors, most of the driving log data has no visual information. Therefore, another solution is necessary to identify traffic regions using only spatial information. To address this challenge, the paper proposes a region recognition method using key primitive transitions obtained from vehicle trajectory information. To obtain the motion primitives, we employed a hierarchical similarity clustering that combines hierarchical clustering and HDP-HSMM. The vehicle behavior from the region of interest was extracted by analyzing the motion primitives transitions located within close range (50 m) of the region. We assessed the behavior of vehicles when approaching two regions: traffic lights and entrances. Three environments were used to evaluate the proposed method, with two different drivers and distinguished layouts. This study shows that the proposed classification method can identify traffic light and entrance regions with an average 89% precision and 86% f-score. Additionally, the hierarchical similarity clustering and window observation approach developed in this study is responsible for increasing the system's precision by 9%.