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Goal-Oriented Pedestrian Motion Prediction
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AI
DOI：10.1109/TITS.2023.3337104.png)
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Abstract
En 中文
Forecasting the motion of others in shared spaces is a key for intelligent agents to operate safely and smoothly. We present an approach for probabilistic prediction of pedestrian motion incorporating various context cues. Our approach is based on goal-oriented prediction, yielding interpretable results for the predicted pedestrian intention, even without the prior knowledge of goal positions. By using Markov chains, the resulting probability distribution is deterministic-a beneficial property for motion planning or risk assessment in automated and assisted driving. Our approach outperforms a physics-based approach and improves over state-of-the-art approaches by reducing standard deviations of prediction errors and improving robustness against realistic, noisy measurements.
Keywords:
Pedestrians
Trajectory
Vehicle dynamics
Planning
Behavioral sciences
Markov processes
Mathematical models
motion planning
probabilistic model
road safety
pedestrian motion prediction
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Citations:
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