Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) require smart built-in systems to recognize pedestrian intention to avoid casualties. Recent approaches towards predicting pedestrian intention use motion information and his/her situational awareness to distinguish between intention of either crossing or stopping before a vehicle. We investigate the power of including contextual information into this problem. Examples of contextual cues include pedestrian position with respect to the curb of a road and relative locations of the vehicles with respect to the pedestrian in the scene. We propose a graphical model Factored Latent Dynamic Conditional Random Fields (FLDCRF) to integrate contextual and motion features of a pedestrian as well as to capture the intrinsic relationship between intention and action of a pedestrian. We show that the inclusion of context information aids early prediction of a pedestrian intention and the motion information provides more accuracy to the predicted output, resulting in an early and accurate joint model to predict pedestrian intention. We also show that FLDCRF performs better over the concatenated motion and context features when compared with existing state-of-the-art machine learning algorithms. The results show accurate estimates of pedestrian intention with high average probabilities (>0.7) as early as 1.2 second before the performed action.
Context based pedestrian intention prediction using factored latent dynamic conditional random fields
2017-11-01
921691 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch