Detection of Cyclist’s Crossing Intention Based on Posture Estimation for Autonomous Driving
Arief Dharmawan Abadi, Yanlei Gu, Igor Goncharenko, Shunsuke Kamijo
Abstract
Improving the safety of bicycle riders is one of the critical issues for autonomous driving. The crossing intention of the cyclist is expected to be predicted from the on- board camera of an autonomous vehicle. In a real traffic situation, a cyclist usually turns his or her head to check the situation of the back of him or her before he or she crosses the road. Therefore, the action of turning the head is an important cue to indicate the intention of crossing a road. This research proposes to estimate the cyclist’s intention based on the body and head orientation using deep neural networks. The proposed system first detects the cyclists and extracts the area of the cyclist from red green blue (RGB) images based on a segmentation neural network. After that, the image of each cyclist is processed by a pose estimation neural network to detect each joint of the cyclist. Subsequently, the heat-map image of each joint of the cyclist is imported into a classification neural network to estimate the body and head orientation. The body orientation and head orientation are jointly used for the prediction of the cyclist’s intention. In a separate process, the cyclist’s position is estimated based on the disparity image generated from a stereo camera. Finally, two results, the cyclist’s position and intention, are integrated to predict the trajectory of the cyclist. A series of experiments have been performed and the experimental results demonstrate that the proposed system has a satisfactory performance. In addition, the comparison experiments show that the model with only heat-map images as input has the best accuracy in the body and head orientation estimation.