An energy-optimal vehicle control system for at least one vehicle including a roadway data source configured for providing traffic and map data including at least one drive segment of the at least one vehicle, and an electrical processing system operably coupled with the roadway data source. The electrical processing system includes an optimizer for generating an energy-optimal speed profile for the at least one drive segment, and the electrical processing system is configured for controlling the speed of the at least one vehicle in accordance with the energy-optimal speed profile.

The present technology relates to an information processing apparatus, an information processing method, a program, a mobile-object control apparatus, and a mobile object that make it possible to appropriately set the accuracy in detecting an object. An information processing apparatus includes a first object detector that performs an object detection on the basis of first sensor data from a first sensor; a second object detector that performs an object detection on the basis of second sensor data from a second sensor that differs in type from the first sensor; a tracking section that predicts a state of a target object that is a tracking target, on the basis of a result of the object detection performed on the basis of the first sensor data, and on the basis of a result of the object detection performed on the basis of the second sensor data; and a detection accuracy controller that sets a high-resolution range on the basis of the state of the target object that is predicted on the basis of the result of the object detection performed on the basis of the first sensor data, and on the basis of the result of the object detection performed on the basis of the second sensor data, the high-resolution range being a range in which an object detection is performed with a higher degree of accuracy than in a range other than the high-resolution range. The present technology is applicable to, for example, a system used to track a target object around a vehicle.

A method is used to control a fuel level gauge of a vehicle system and includes: monitoring, via an engine controller, a fuel economy of the vehicle system; comparing, via the engine controller, the fuel economy with a predetermined fuel economy threshold to determine whether the fuel economy is less than the predetermined fuel economy threshold; adjusting, via the engine controller, a sensitivity of a display filter in response to determining that the fuel economy is less than the predetermined fuel economy threshold for a predetermined amount of time in order to maximize an accuracy of the fuel level gauge, wherein the display filter smoothes an unfiltered fuel level signal received from a fuel level sensor of the vehicle system, thereby generating a filtered fuel level signal; and controlling, via an instrument panel controller, the fuel level gauge of the vehicle system.

The techniques discussed herein include modifying a Kalman filter to additionally include a loss component that dampens the effect measurements with large errors (or measurements indicating states that are rather different than the predicted state) have on the Kalman filter and, in particular, the updated uncertainty and/or updated prediction. In some examples, the techniques include scaling a Kalman gain based at least in part on a loss function that is based on the innovation determined by the Kalman filter. The techniques additionally or alternatively include a reformulation of a Kalman filter that ensures that the uncertainties determined by the Kalman filter remain symmetric and positive definite.

In a torque distribution method for a vehicle, torque is optimally distributed to front and rear wheels of a vehicle by reflecting pitch motion characteristics and longitudinal load movement information of the vehicle in real time. The repetition of wheel slip and the degradation of wheel slip control performance caused by the pitch motion are reduced. The longitudinal load movement of the vehicle is reduced.

Systems and methods for personalizing adaptive cruise control in a vehicle are disclosed herein. One embodiment collects vehicle-following-behavior data associated with a particular driver; trains a Gaussian Process (GP) Regression model using the collected vehicle-following-behavior data to produce a set of adaptive-cruise-control (ACC) parameters pertaining to the particular driver, the set of ACC parameters modeling learned vehicle-following behavior of the particular driver; generates an acceleration command for the vehicle based, at least in part, on the set of ACC parameters; applies a predictive safety filter to the acceleration command to produce a certified acceleration command that has been vetted for safety; and controls acceleration of the vehicle automatically in accordance with the certified acceleration command to regulate a following distance between a lead vehicle and the vehicle in accordance with the learned vehicle-following behavior of the particular driver.

A method for determining the direction of travel of a vehicle comprises providing a first sensor for measuring a longitudinal acceleration of the vehicle and at least one second sensor for establishing the rotational movement of a wheel of the vehicle, An acceleration signal containing acceleration information from the first sensor is received by the system. The acceleration signal is filtered resulting in a modified acceleration signal. The direction of travel of the vehicle is determined based on the modified acceleration signal and based on the output signal of the second sensor.

A scene simulation system can use scene data of a scene of a vehicle to generate one or more simulated states and one or more simulated trajectories associated with the one or more simulated states. The system can evaluate the simulated trajectories and select an action for the vehicle based on the evaluation of the simulated trajectories.

In various examples, perception-based parking assistance systems and methods for an ego-machine are presented. Example embodiments may determine a location of a real-world parking strip relative to an ego-machine and an associated parking rule for the parking strip. A virtual parking strip and one or more virtual parking signs may be generated based at least in part on one or more detected features in an environment of the ego-machine and a tracked motion of the ego machine, and the virtual parking strip may be used to track parking strip locations and associated parking rules. The virtual parking strips and associated rules may be relied upon by an ego-machine to determine parking locations and/or to navigate into a suitable parking spot.

A method for controlling a driving force of a vehicle includes setting and providing a filter simulation map for simulating a filter to a control unit of the vehicle, determining a required driving force command based on vehicle driving information collected while the vehicle is driven, determining a final front wheel driving force command and a final rear wheel driving force command from the determined required driving force command through a limit application process of using a limit determined in accordance with a vehicle driving variable in the filter simulation map, and controlling a driving force which is applied to front wheels and rear wheels of the vehicle by a driving device configured to drive the vehicle in accordance with the determined final front wheel driving force command and the determined final rear wheel driving force command.