  This paper proposes an interaction and safety-aware motion-planning method
for an autonomous vehicle in uncertain multi-vehicle traffic environments. The
method integrates the ability of the interaction-aware interacting multiple
model Kalman filter (IAIMM-KF) to predict interactive multi-modal maneuvers of
surrounding vehicles, and the advantage of model predictive control (MPC) in
planning an optimal trajectory in uncertain dynamic environments. The
multi-modal prediction uncertainties, containing both the maneuver and
trajectory uncertainties of surrounding vehicles, are considered in computing
the reference targets and designing the collision-avoidance constraints of MPC
for resilient motion planning of the ego vehicle. The MPC achieves safety
awareness by incorporating a tunable parameter to adjust the predicted obstacle
occupancy in the design of the safety constraints, allowing the approach to
achieve a trade-off between performance and robustness. Based on the prediction
of the surrounding vehicles, an optimal reference trajectory of the ego vehicle
is computed by MPC to follow the time-varying reference targets and avoid
collisions with obstacles. The efficiency of the method is illustrated in
challenging highway-driving simulation scenarios and a driving scenario from a
recorded traffic dataset.
