37,556 research outputs found
A Model-Predictive Motion Planner for the IARA Autonomous Car
We present the Model-Predictive Motion Planner (MPMP) of the Intelligent
Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses
a path planner to compute a path from its current position to the desired
destination. Using this path, the current position, a goal in the path and a
map, IARA's MPMP is able to compute smooth trajectories from its current
position to the goal in less than 50 ms. MPMP computes the poses of these
trajectories so that they follow the path closely and, at the same time, are at
a safe distance of eventual obstacles. Our experiments have shown that MPMP is
able to compute trajectories that precisely follow a path produced by a Human
driver (distance of 0.15 m in average) while smoothly driving IARA at speeds of
up to 32.4 km/h (9 m/s).Comment: This is a preprint. Accepted by 2017 IEEE International Conference on
Robotics and Automation (ICRA
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
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