Real-Time Certified Probabilistic Pedestrian Forecasting
| Autor: | Henry O. Jacobs, Owen K. Hughes, Matthew Johnson-Roberson, Ram Vasudevan |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Control and Optimization Computer science Embarrassingly parallel Biomedical Engineering Markov process 02 engineering and technology Pedestrian Certification Machine learning computer.software_genre symbols.namesake 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Simulation business.industry Mechanical Engineering Probabilistic logic Computer Science Applications Human-Computer Interaction Control and Systems Engineering Ordinary differential equation symbols 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Probabilistic forecasting Artificial intelligence business computer |
| Zdroj: | IEEE Robotics and Automation Letters. 2:2064-2071 |
| ISSN: | 2377-3774 |
| DOI: | 10.1109/lra.2017.2719762 |
| Popis: | The success of autonomous systems will depend upon their ability to safely navigate human-centric environments. This motivates the need for a real-time, probabilistic forecasting algorithm for pedestrians, cyclists, and other agents since these predictions will form a necessary step in assessing the risk of any action. This letter presents a novel approach to probabilistic forecasting for pedestrians based on weighted sums of ordinary differential equations that are learned from historical trajectory information within a fixed scene. The resulting algorithm is embarrassingly parallel and is able to work at real-time speeds using a naive Python implementation. The quality of predicted locations of agents generated by the proposed algorithm is validated on a variety of examples and is considerably higher than existing state of the art approaches over long time horizons. |
| Databáze: | OpenAIRE |
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