Accurate and robust recognition and prediction of traffic situation plays an important role in autonomous driving, which is a prerequisite for risk assessment and effective decision making. Although there exist a lot of works dealing with modeling driver behavior of a single object, it remains a challenge to make predictions for multiple highly interactive agents that react to each other simultaneously. In this work, we propose a generic probabilistic hierarchical recognition and prediction framework which employs a two-layer Hidden Markov Model (TLHMM) to obtain the distribution of potential situations and a learning-based dynamic scene evolution model to sample a group of future trajectories. Instead of predicting motions of a single entity, we propose to get the joint distribution by modeling multiple interactive agents as a whole system. Moreover, due to the decoupling property of the layered structure, our model is suitable for knowledge transfer from simulation to real world applications as well as among different traffic scenarios, which can reduce the computational efforts of training and the demand for a large data amount. A case study of highway ramp merging scenario is demonstrated to verify the effectiveness and accuracy of the proposed framework.
We present the use of layered probabilistic representations using Hidden Markov Models for performing sensing, learning, and inference at multiple levels of temporal granularity. We describe the use of the representation in a system that diagnoses states of a user's activity based on real-time streams of evidence from video, acoustic, and computer interactions. We review the representation, present an implementation, and report on experiments with the layered representation in an office-awareness application. We focus on a mobile robot that must learn another robot's motion model from observations to track it in a given map. This problem has several real-world applications such as self-driving cars being electronically towed by other cars and for telepresence robots. Our context is a nested particle filter, a generalization of the traditional particle filter, that allows both self-localization and tracking of another robot simultaneously. While the robot's observations are used to weight nested particles, the problem arises during the propagation step of the nested particles during which a motion model is needed. We introduce a novel layered hidden Markov model for this problem and present an on-line algorithm which learns the HMM parameters from observations gathered during the run. We demonstrate significantly improved tracking accuracy on using this new model to predict the motion of a leading mobile robot, in comparison to pre-defined and random motion models as previously used in literature. 