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:我们建议对自主车辆采用模型预测控制办法,利用高斯人学的流程预测人类驾驶行为。拟议办法利用GP预测的不确定性来实现安全。多模式预测办法考虑人驾驶员的可能意图。虽然用不同的高斯人手流程表示意图,但观察到的行为所预见的概率由适当的在线分类决定。低于某种概率阈值的意向被忽略来改进性能。提议的多模式模型预测控制办法与高斯人手流程回归支持相结合,可以使反复出现可行性和概率约束性制约满意度高几率。在模拟中强调这一办法,考虑对高斯人流程的培训进行真实世界测量。</s>