  Continuous optimization based motion planners require deciding on a maneuver
homotopy before optimizing the trajectory. Under uncertainty, maneuver
intentions of other participants can be unclear, and the vehicle might not be
able to decide on the most suitable maneuver. This work introduces a method
that incorporates multiple maneuver preferences in planning. It optimizes the
trajectory by considering weighted maneuver preferences together with
uncertainties ranging from perception to prediction while ensuring the
feasibility of a chance-constrained fallback option. Evaluations in both
driving experiments and simulation studies show enhanced interaction
capabilities and comfort levels compared to conventional planners, which
consider only a single maneuver.
