关键词: 交通工程；驾驶意图识别；灯语识别；自动驾驶；Mask R-CNN；交互行为

Abstract: In view of the existing traffic situation of hybrid driving of autonomous vehicle (AV) and human-driven vehicle (HV) for a long time, a vehicle driving intention recognition model based on the taillight status in mixed traffic environment was proposed by combining deep learning and HSV color characteristic extraction method. The proposed model was limited by the region of interest (RoI) area detected by the Mask R-CNN vehicle detection model, and HV in this area was taken as the research object. According to the correlation of taillight positions, vertical and horizontal constraints were added in the rear area of HV to locate the taillights which transmitted the signal of light status. A taillight status recognition algorithm based on time sequence was proposed on the basis of the specified combination of light status and the flashing frequency of turn signals. The CSRT (channel and spatial relatability tracking) was used to track HV taillights and count the timing status of taillights. With the dynamical lights status as input, a driving intention recognition model based on taillight status was constructed. The Cityspaces dataset and traffic flow video data based on real road conditions were used to train, validate and test the proposed model. The research results show that the accuracy and recall rate for video stream vehicle taillight detection of the driving intention recognition model based on the taillight status are 96.0% and 98.2% respectively, the average accuracy rate of the driving intention recognition reaches 95.9%, and the recognition time of single frame is 20 ms. The proposed method provides an effective theoretical basis for AV decision-making and planning in high-speed mixed traffic environments.

Key words: transportation engineering; driving intention recognition; light status recognition; autonomous driving; Mask R-CNN; interactive behavior