Traxen Inc.

Arvin Meritor Technology, LLC

Ali Maleki

Gerti Tuzi

Syed Ali

Saeed Zamanzad Gavidel

Naren Reddy Dhansri

Nabil H K Hirzallah

Michael Makowski

Vern A Caron

Paul C Guenther

Jeffrey Schoenhals

Prem Kumar Sivakumar

Nabil Hk Hirzallah

Martha Elisa Rodriguez Zulueta

Larry Deal

Mahmood Karimi

A computing device including a plurality of sensors, a system-on-module, a safety microcontroller and a plurality of communication interfaces communicatively coupling the system-on-module, the safety microcontroller and the plurality of sensors together. The system-on module can include an integrated interconnection of a plurality of different types of cores and one or more different types of memory. The system-on module can be configured to control operation and or performance of a system. The safety microcontroller can be configured to provide safety supervision of the system-on-module.

Safety supervised general purpose computing devices

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.

Automated cruise control system

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.

Normalized performance comparison techniques

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.

Predictive control techniques for ground vehicles

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. The performance indicator can be configured to convey information associated with the various aspects of vehicle operation.

User vehicle operation interface systems and methods

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.

Pre-training of a reinforcement learning ground vehicle controller using monte carlo simulation

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.

Reinforcement learning based ground vehicle control techniques

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.

Ground vehicle control techniques

An interface device system includes a first transceiver, a second transceiver, and a controller for receiving an input signal and detecting whether a predetermined type of network connection exists. The controller selectively transmits a first signal or a second signal that corresponds to the input signal. The controller transmits the first signal over the first transceiver in response to absence of the predetermined type of network connection. The controller transmits the second signal over a second transceiver in response to existence of the predetermined type of network connection.

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Plymouth, MI, United States of America

Ali Maleki