Feature-Based Prediction of Trajectories for Socially Compliant Navigation
Open accessAbstract
Mobile robots that operate in a shared environment with humans need the ability to predict the movements of people to better plan their navigation actions.In this paper, we present a novel approach to predict the movements of pedestrians.Our method reasons about entire trajectories that arise from interactions between people in navigation tasks.It applies a maximum entropy learning method based on features that capture relevant aspects of the trajectories to determine the probability distribution that underlies human navigation behavior.Hence, our approach can be used by mobile robots to predict forthcoming interactions with pedestrians and thus react in a socially compliant way.In extensive experiments, we evaluate the capability and accuracy of our approach and demonstrate that our algorithm outperforms the popular social forces method, a state-of-the-art approach.Furthermore, we show how our algorithm can be used for autonomous robot navigation using a real robot.
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Previous works on anticipation usually deal with single-data modality and do not address anticipation for sensory-rich robotics applications.
“Previous works on anticipation [20, 21, 24] usually deal with single-data modality and do not address anticipation for sensory-rich robotics applications.”
From Recurrent Neural Networks for driver activity anticipation via sensory-fusion architecture (2016) · cites this paper as [24]
The Real-World Robot Experiments section describes tracking human and robot poses with a visual motion-capture system, consistent with a single data modality rather than a sensory-rich setup. But the paper develops pedestrian-motion prediction for robot navigation and tests it on a real robot, so it does address anticipation in a robotics application. The cited work alone also cannot establish what previous works 'usually' do. (LLM, conf=0.80)
Experimental — accuracy not yet measuredForecasting human navigation trajectories has enabled robots to plan sociable trajectories around humans.
“Similarly, forecasting human navigation trajectories has enabled robots to plan sociable trajectories around humans [20, 6, 24].”
From Recurrent Neural Networks for driver activity anticipation via sensory-fusion architecture (2016) · cites this paper as [24]
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- Authors
- Kuderer, M., Kretzschmar, H., Sprunk, C., & Burgard, W.
- Affiliations
- University of Freiburg (DE)
- ORCID
- Burgard, Wolfram
- Venue
- Robotics: Science and Systems VIII
- Published
- 2012-07-09
- OpenAlex
- W630739203
- Cited by
- 214
- References
- 32
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 5 · 2023: 9 · 2022: 14 · 2021: 23 · 2020: 31 · 2019: 21 · 2018: 25 · 2017: 17 · 2016: 21 · 2015: 16 · 2014: 14 · 2013: 9 · 2012: 1
- FWCI
- 22.59
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Robotics: Science and Systems (publishedVersion)
- Topics
- Physical Sciences › Engineering › Ocean Engineering › Evacuation and Crowd Dynamics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- pedestrian trajectory prediction, human-robot interaction, mobile robots, feature-based prediction, autonomous robot navigation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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