We propose a model predictive control approach for autonomous vehicles that exploits learned gaussianprocesses (GPs) for predicting human driving behavior. The proposed approach employs the uncertainty about the GP&#...
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ISBN:
(纸本)9781713872344
We propose a model predictive control approach for autonomous vehicles that exploits learned gaussianprocesses (GPs) for predicting human driving behavior. The proposed approach employs the uncertainty about the GP's prediction to achieve safety. The multi-mode predictive control approach considers the possible intentions of the human drivers. While the intentions are represented by different gaussianprocesses, their probabilities foreseen in the observed behaviors are determined by a suitable online classification. Intentions below a certain probability threshold are neglected to improve performance. The proposed multi-mode model predictive control approach with gaussianprocess regression support enables probabilistic constraint satisfaction with high probability. The approach is underlined in simulation, considering real-world measurements for training the gaussianprocesses. Copyright (c) 2023 The Authors.
We propose a model predictive control approach for autonomous vehicles that exploits learned gaussianprocesses (GPs) for predicting human driving behavior. The proposed approach employs the uncertainty about the GP&#...
详细信息
We propose a model predictive control approach for autonomous vehicles that exploits learned gaussianprocesses (GPs) for predicting human driving behavior. The proposed approach employs the uncertainty about the GP's prediction to achieve safety. The multi-mode predictive control approach considers the possible intentions of the human drivers. While the intentions are represented by different gaussianprocesses, their probabilities foreseen in the observed behaviors are determined by a suitable online classification. Intentions below a certain probability threshold are neglected to improve performance. The proposed multi-mode model predictive control approach with gaussianprocess regression support enables probabilistic constraint satisfaction with high probability. The approach is underlined in simulation, considering real-world measurements for training the gaussianprocesses.
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