• Corpus ID: 195707955

Incremental Learning for Motion Prediction of Pedestrians and Vehicles

  title={Incremental Learning for Motion Prediction of Pedestrians and Vehicles},
  author={Alejandro Dizan Vasquez Govea},
Abstract The main subject of this thesis is motion prediction. The problem is approached from the hypothesis that the dynamic and kinematic properties of objects such as pedestrian and vehicles do not suffice to predict their motion in the long term. Instead, the work presented here, in scribes itself in a new family of approaches which assume that, in a given environment, objects do not move at random, but engage in “typical motion patterns”, which may be learned and then used to predict motion… 

Incremental Learning of Statistical Motion Patterns With Growing Hidden Markov Models

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A model-based approach is introduced to interpret the laser measurement sequence over a sliding window of time by hypotheses of moving object trajectories by exploring the data-driven Markov chain Monte Carlo technique.

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Optimal motion planning with reachable sets of vulnerable road users

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A self-learning prototype system for the real-time detection of unusual motion patterns and motion recognition based on the same method, the extended Condensation algorithm, used for the object tracking.

Learning Motion Patterns of People for Compliant Robot Motion

A technique for learning collections of trajectories that characterize typical motion patterns of persons and how to incorporate the probabilistic belief about the potential trajectories of persons into the path planning process of a mobile robot is proposed.

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  • D. VasquezThierry Fraichard
  • Computer Science
    IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004
  • 2004
A technique to obtain long term estimates of the motion of a moving object in a structured environment by observing the environment and clustering the observed trajectories using any pairwise clustering algorithm.

Moving object prediction for off-road autonomous navigation

A combined probabilistic object classification and estimation theoretic framework to predict the future location of moving objects, along with an associated uncertainty measure is outlined.

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Camera-based observation of obstacle motions to derive statistical data for mobile robot motion planning

  • E. KruseF. Wahl
  • Computer Science
    Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146)
  • 1998
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