  Lane changing and lane merging remains a challenging task for autonomous
driving, due to the strong interaction between the controlled vehicle and the
uncertain behavior of the surrounding traffic participants. The interaction
induces a dependence of the vehicles' states on the (stochastic) dynamics of
the surrounding vehicles, increasing the difficulty of predicting future
trajectories. Furthermore, the small relative distances cause traditional
robust approaches to become overly conservative, necessitating control methods
that are explicitly aware of inter-vehicle interaction. Towards these goals, we
propose an interaction-aware stochastic model predictive control (MPC) strategy
integrated with an online learning framework, which models a given driver's
cooperation level as an unknown parameter in a state-dependent probability
distribution. The online learning framework adaptively estimates the
surrounding vehicle's cooperation level with the vehicle's past trajectory and
combines this with a kinematic vehicle model to predict the probability of a
multimodal future state trajectory. The learning is conducted with logistic
regression which enables fast online computation. The multi-future prediction
is used in the MPC algorithm to compute the optimal control input while
satisfying safety constraints. We demonstrate our algorithm in an interactive
lane changing scenario with drivers in different randomly selected cooperation
levels.
