Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

2015 Annual Report Transportation Research Center for Livable Communities

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Adaptive tactical behaviour planner for autonomous ground vehicle

Success of autonomous vehicle to effectively replace a human driver depends on its ability to plan safe, efficient and usable paths in dynamically evolving traffic scenarios. This challenge gets more difficult when the autonomous vehicle has to drive through scenarios such as intersections that demand interactive behavior for successful navigation. The many autonomous vehicle demonstrations over the last few decades have highlighted the limitations in the current state of the art in path planning solutions. They have been found to result in inefficient and sometime unsafe behaviours when tackling interactively demanding scenarios. In this paper we review the current state of the art of path planning solutions, the individual planners and the associated methods for each planner. We then establish a gap in the path planning solutions by reviewing the methods against the objectives for successful path planning. A new adaptive tactical behaviour planner framework is then proposed to fill this gap. The behaviour planning framework is motivated by how expert human drivers plan their behaviours in interactive scenarios. Individual modules of the behaviour planner is then described with the description how it fits in the overall framework. Finally we discuss how this planner is expected to generate safe and efficient behaviors in complex dynamic traffic scenarios by considering a case of an un-signalised roundabout