A system for controlling a vehicle, comprising: an input interface configured to accept a current state of the vehicle, an image of an environment in proximity to the current state of the vehicle, and a destination of the vehicle; a memory configured to store a probabilistic motion planner and an adaptive predictive controller , wherein the probabilistic motion planner is configured to accept the current state of the vehicle, the destination of the vehicle, and the image of the environment to produce a sequence of parametric probability distributions over a sequence of target states defining a motion plan for the vehicle, wherein parameters of each parametric probability distribution define a first order moment and at least one higher order moment of the probability distribution, wherein the adaptive predictive controller is configured to optimize a cost function over a prediction horizon to produce a sequence of control commands to one or multiple actuators of the vehicle, wherein the optimization of the cost function balances a cost of tracking of different state variables in the sequence of the target states defined by the first moments, wherein the different state variables are weighted using one or multiple of the higher order moments of the probability distribution in the balancing of the cost of tracking; a processor configured to execute the probabilistic motion planner by submitting the current state of the vehicle, the destination of the vehicle, and the image of the environment to the probabilistic motion planner and configured to execute the adaptive predictive controller by submitting the sequence of the parametric probability distributions produced by the probabilistic motion planner to the adaptive predictive controller to produce the sequence of control commands; and an output interface configured to output at least one control command determined by the adaptive predictive controller to at least one actuator of the vehicle. an input interface configured to accept a current state of the vehicle, an image of an environment in proximity to the current state of the vehicle, and a destination of the vehicle; a memory configured to store a probabilistic motion planner and an adaptive predictive controller , wherein the probabilistic motion planner is configured to accept the current state of the vehicle, the destination of the vehicle, and the image of the environment to produce a sequence of parametric probability distributions over a sequence of target states defining a motion plan for the vehicle, wherein parameters of each parametric probability distribution define a first order moment and at least one higher order moment of the probability distribution, wherein the adaptive predictive controller is configured to optimize a cost function over a prediction horizon to produce a sequence of control commands to one or multiple actuators of the vehicle, wherein the optimization of the cost function balances a cost of tracking of different state variables in the sequence of the target states defined by the first moments, wherein the different state variables are weighted using one or multiple of the higher order moments of the probability distribution in the balancing of the cost of tracking; a processor configured to execute the probabilistic motion planner by submitting the current state of the vehicle, the destination of the vehicle, and the image of the environment to the probabilistic motion planner and configured to execute the adaptive predictive controller by submitting the sequence of the parametric probability distributions produced by the probabilistic motion planner to the adaptive predictive controller to produce the sequence of control commands; and an output interface configured to output at least one control command determined by the adaptive predictive controller to at least one actuator of the vehicle. <EOS>