  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.
