An Incremental Probabilistic Model to Predict Bus Bunching in Real-Time

For robots to execute their navigation tasks both fast and safely in the presence of humans, it is necessary to make predictions about the route those humans intend to follow. Within this work, a model-based method is proposed that relates human motion behavior perceived from RGBD input to the constraints imposed by the environment by considering typical human routing alternatives. Multiple hypotheses about routing options of a human towards local semantic goal locations are created and validated, including explicit collision avoidance routes. It is demonstrated, with real-time, real-life experiments, that a coarse discretization based on the semantics of the environment suffices to make a proper distinction between a person going, for example, to the left or the right on an intersection. As such, a scalable and explainable solution is presented, which is suitable for incorporation within navigation algorithms.

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Performance Evaluation of a Real-time Simulation Architecture using Probabilistic Model Checking

Electronic Notes in Theoretical Computer Science ◽

10.1016/j.entcs.2005.01.010 ◽

2005 ◽

Vol 128 (4) ◽

pp. 3-24 ◽

Cited By ~ 1

Author(s):

Nil Geisweiller ◽

Jeremie Bonte

Keyword(s):

Performance Evaluation ◽

Model Checking ◽

Real Time ◽

Probabilistic Model ◽

Probabilistic Model Checking ◽

Real Time Simulation ◽

Time Simulation

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A dispatching operation risk assessment method based on real-time probabilistic model

2013 IEEE International Conference of IEEE Region 10 (TENCON 2013) ◽

10.1109/tencon.2013.6718497 ◽

2013 ◽

Author(s):

Jianing Liu ◽

Shizhao Hu ◽

Dong Chen ◽

Bo Li

Keyword(s):

Risk Assessment ◽

Real Time ◽

Probabilistic Model ◽

Assessment Method ◽

Risk Assessment Method ◽

Operation Risk

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Design of Real—Time Sampling Strategies for Submerged Oil Based on Probabilistic Model Predictions

Journal of Marine Science and Engineering ◽

10.3390/jmse8120984 ◽

2020 ◽

Vol 8 (12) ◽

pp. 984

Author(s):

Chao Ji ◽

James D. Englehardt ◽

Cynthia Juyne Beegle-Krause

Keyword(s):

Real Time ◽

Oil Spill ◽

Probabilistic Model ◽

Sampling Method ◽

Sampling Methods ◽

Second Phase ◽

Sampled Data ◽

Sampling Plans ◽

Phase Sampling ◽

Oil Spill Response

Locating and tracking submerged oil in the mid depths of the ocean is challenging during an oil spill response, due to the deep, wide-spread and long-lasting distributions of submerged oil. Due to the limited area that a ship or AUV can visit, efficient sampling methods are needed to reveal the real distributions of submerged oil. In this paper, several sampling plans are developed for collecting submerged oil samples using different sampling methods combined with forecasts by a submerged oil model, SOSim (Subsurface Oil Simulator). SOSim is a Bayesian probabilistic model that uses real time field oil concentration data as input to locate and forecast the movement of submerged oil. Sampling plans comprise two phases: the first phase for initial field data collection prior to SOSim assessments, and the second phase based on the SOSim assessments. Several environmental sampling techniques including the systematic random, modified station plans as well zig-zag patterns are evaluated for the first phase. The data using the first phase sampling plan are then input to SOSim to produce submerged oil distributions in time. The second phase sampling methods (systematic random combined with the kriging-based sampling method and naive zig-zag sampling method) are applied to design the sampling plans within the submerged oil area predicted by SOSim. The sampled data obtained using the second phase sampling methods are input to SOSim to update the model’s assessments. The performance of the sampling methods is evaluated by comparing SOSim predictions using the sampled data from the proposed sampling methods with simulated submerged oil distributions during the Deepwater Horizon spill by the OSCAR (oil spill contingency and response) oil spill model. The proposed sampling methods, coupled with the use of the SOSim model, are shown to provide an efficient approach to guide oil spill response efforts.

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