We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes for predicting human driving behavior. The proposed approach employs the uncertainty about the GP's prediction to achieve safety. A multi-mode predictive control approach considers the possible intentions of the human drivers. While the intentions are represented by different Gaussian processes, 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 Gaussian process regression support enables repeated feasibility and probabilistic constraint satisfaction with high probability. The approach is underlined in simulation, considering real-world measurements for training the Gaussian processes.

翻译：我们提出了一种面向自主车辆的模型预测控制方法，该方法利用学习得到的高斯过程来预测人类驾驶行为。所提方法利用高斯过程预测的不确定性来保障安全性。一种多模式预测控制方法考虑了人类驾驶员的潜在意图。虽然意图由不同的高斯过程表示，但通过适当的在线分类来确定其在观测行为中出现的概率。为提高性能，低于特定概率阈值的意图被忽略。所提出的基于高斯过程回归支持的多模式模型预测控制方法能够实现迭代可行性和高概率的概率约束满足。该方法在仿真中进行了验证，并采用真实世界测量数据来训练高斯过程。