D
Dizan Vasquez
Researcher at French Institute for Research in Computer Science and Automation
Publications - 32
Citations - 2061
Dizan Vasquez is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Motion planning & Hidden Markov model. The author has an hindex of 16, co-authored 32 publications receiving 1677 citations. Previous affiliations of Dizan Vasquez include Monterrey Institute of Technology and Higher Education & École Polytechnique Fédérale de Lausanne.
Papers
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Journal ArticleDOI
A survey on motion prediction and risk assessment for intelligent vehicles
TL;DR: This paper points out the tradeoff between model completeness and real-time constraints, and the fact that the choice of a risk assessment method is influenced by the selected motion model.
Proceedings ArticleDOI
Inverse Reinforcement Learning algorithms and features for robot navigation in crowds: An experimental comparison
TL;DR: A new software framework to systematically investigate the effect features and learning algorithms used in the literature is introduced and results for the task of socially compliant robot navigation in crowds are presented, evaluating two different IRL approaches and several feature sets in large-scale simulations.
Proceedings ArticleDOI
Motion prediction for moving objects: a statistical approach
Dizan Vasquez,Thierry Fraichard +1 more
TL;DR: 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.
Journal ArticleDOI
Growing Hidden Markov Models: An Incremental Tool for Learning and Predicting Human and Vehicle Motion
TL;DR: This work presents an approach where motion patterns can be learned incrementally, and in parallel with prediction, based on a novel extension to hidden Markov models, called growing hidden MarkOV models, which gives the ability to learn incrementally both the parameters and the structure of the model.
Journal ArticleDOI
Incremental Learning of Statistical Motion Patterns With Growing Hidden Markov Models
TL;DR: This work presents an approach where motion patterns can be learned incrementally and in parallel with prediction, based on a novel extension to hidden Markov models (HMMs) - called growing hidden MarkOV models - which gives the ability to incrementally learn both the parameters and the structure of the model.