Abstract: A normalized driver performance comparison framework can include predicting a plurality of scenarios of a ground vehicle. A Monte Carlo simulation can be utilized to determine results using the predicted plurality of scenarios and one or more predictive models. Best and worst possible scenarios and associated efficiency for an observed scenario can be determined based on the Monte Carlo simulation results. The efficiency for the observed scenario can be normalized as a function of the best and worst efficiencies.

Abstract: A ground vehicle control system including a plurality of sensors and one or more predictive controllers. The sensors can be configured to detect environment information and vehicle operating information. The one or more predictive controllers can be configured to self-train for an energy consumption solution based on one or more control parameters including the environment information and the vehicle operating information.

Abstract: Presented systems and methods facilitate efficient and effective performance of vehicle operations. In one embodiment, a system comprises a user interface, a processor, and a memory. The user interface is configured to convey information associated with operation of a vehicle to and from a user, wherein the information associated with the operation of the vehicle includes information associated with a performance objective. Information associated with the operation of the vehicle can include metric information that expresses a characteristic corresponding to the performance objective. The metric information can be associated with various aspects (e.g., current, future, etc.) of the vehicle operation. Processing by the processor can include comparative analysis of actual performance of the vehicle operation to target values associated with the vehicle operation. The user interface can include a performance indicator.

Abstract: Techniques for utilizing a Monte Carlo model to perform pre-training of a ground vehicle controller. A sampled distribution of actions and corresponding states can be utilized to train a reinforcement learning controller policy, learn an action-value function, or select a set of control parameters with a predetermined loss.

Abstract: Reinforcement learning based ground vehicle control techniques adapted to reduce energy consumption, braking, shifting, travel distance, travel time, and or the like. The reinforcement learning techniques can include training a reinforcement learning controller based on a simulated ground vehicle environment during a simulation mode, and then further training the reinforcement learning controller based on a ground vehicle environment during an operating mode of a ground vehicle.

Abstract: Ground vehicle control techniques adapted to reduce energy consumption, braking, shifting, travel distance, travel time, and or the like. The techniques can generate a target speed window and a target vehicle performance plan for controlling operation of a ground vehicle along a current and one or more upcoming segments of a roadway responsive to the dynamic driving environment.

Abstract: An automated cruise control system is disclosed. Sensors detect driving parameters associated with the ground vehicle as the ground vehicle maneuvers along a segment of the roadway. A ground vehicle control detector detects ground vehicle control inputs associated with an operation of the ground vehicle. The ground vehicle control inputs are generated from a longitudinal operation of the ground vehicle. A energy consumption cruise controller automatically adjusts the operation of the ground vehicle as the ground vehicle maneuvers along the segment of the roadway to maintain the operation of the ground vehicle within an operation threshold based on the detected driving parameters and ground vehicle control inputs. The operation threshold is the operation of the ground vehicle that decreases an amount of overall energy consumed by the ground vehicle and maintains a longitudinal speed of the ground vehicle within a longitudinal speed threshold.