Disclosed is a method for calculating a control setpoint of a hybrid powertrain of a motor vehicle, the hybrid powertrain including an electric motor and an internal combustion engine (ICE) that is equipped with a gearbox and that is supplied with fuel. The method includes: acquiring a value relative to a power requested at the vehicle's drive wheels; and determining the contribution of the electric motor and the ICE in order to satisfy the request for power at the drive wheels. The determination step involves calculating a triplet of three values, one value relating to the electromechanical power that the electric motor must provide, one value relating to the thermomechanical power that the ICE must provide and one value relating to the ratio that needs to be engaged in the gearbox, this triplet minimising the fuel consumption of the ICE and the current consumption of the electric motor.

A method controls an energy equivalence factor of a motor vehicle including a heat engine and at least one electric motor powered by a storage battery. The method includes estimating a value of the energy equivalence factor proportional to a predetermined maximum value when the difference is lower than the threshold value or proportional to a predetermined minimum value when the difference is higher than the threshold value.

In an operational mode of a vehicle, a vehicle system can be influenced by a mix of autonomous control inputs and manual control inputs. A first weight can be assigned to manual control inputs, and a second weight can be assigned to autonomous control inputs. While the vehicle is being operated primarily by manual inputs from a human driver, it can be determined whether the human driver of the vehicle has made a driving error and whether a current driving environment of the vehicle is a low complexity driving environment. Responsive to determining that the human driver of the vehicle has made the driving error and to determining that the current driving environment of the vehicle is a low complexity driving environment, the second weight assigned to autonomous control inputs can be automatically increased. Autonomous control inputs can influence the vehicle system in an amount corresponding to the second weight.

A system for tuning a trajectory tracking controller for a vehicle includes a trajectory planner configured to generate the planned trajectory and to output one or more planned trajectory components representative of the planned trajectory, a model predictive controller including an internal model and an optimizer, and a tuning neural network configured to receive the one or more planned trajectory components and one or more measured trajectory components and to produce weights for a cost function. The internal model is configured to receive a predicted control input from the optimizer and the one or more measured trajectory components and to produce a predicted output. The optimizer utilizes a cost function and is configured to receive the weights for the cost function and a predicted error and to produce the predicted control input, wherein the predicted error is a selected one of the planned trajectory components minus the predicted output.

A hybrid powertrain system includes an engine and an electric machine respectively connected to first and second drive axles, with the electric machine decoupled from the engine. The system includes a battery pack and a controller. The controller has slip integrators with a corresponding integrator value for a given one of the drive axles. The integrator values are indicative of an accumulated amount of drive wheel slip over a calibrated duration or window. The integrator values change responsive to axle torque and traction control status signal. The integrator values are added together to derive an integrator sum. Responsive to the integrator sum exceeding a calibrated integrator threshold, the controller executes a control action, including automatically executing a Weather Mode in which energy use of the battery pack is reserved for traction control/propulsion of the vehicle.

A modified Kalman filter may include one or more neural networks to augment or replace components of the Kalman filter in such a way that the human interpretability of the filter's inner functions is preserved. The neural networks may include a neural network to account for bias in measurement data, a neural network to account for unknown controls in predicting a state of an object, a neural network ensemble that is trained differently based on different sensor data, a neural network for determining the Kalman gain, and/or a set of Kalman filters including various neural networks that determine independent estimated states, which may be fused using Bayesian fusion to determine a final estimated state.

Provided is a mobile object control device including: a storage medium having computer-readable instructions stored therein; and a processor connected to the storage medium, wherein the processor executes the computer-readable instructions to recognize an object which is located near a mobile object, set a risk which is an index value indicating a degree to which the mobile object should avoid entry on the basis of a position of the object, and generate a target trajectory for the mobile object to travel along so as to pass through a point at which the risk is low, and generating the target trajectory includes setting a plurality of first observation points at intervals in a traveling direction of the mobile object, setting one or more second observation points in each of a left direction and a right direction as seen from the mobile object for each of the plurality of first observation points, and searching for a point at which the risk is low on the basis of the risk at an observation point group including a first observation point and a second observation point corresponding to each other.

A control calculation apparatus (42) includes a target-trajectory generation section (421) that generates a target trajectory including a target route (T1) for a vehicle (40), based on information around the vehicle (40); a prediction time period setting section (422) that sets a prediction time period for predicting states of the vehicle (40), based on the target route (T1); and a control calculation section (423) that calculates target control values for making the vehicle (40) keep track of the target trajectory in the prediction time period, and then outputs the target control values to a control section (5) that controls the vehicle (40).

The present technology is directed to dynamically adjusting an autonomous vehicle (AV) system based on deep learning optimizations. An AV management system can generate a downscaling signal based on a result of comparing a complexity of an environment for an AV to navigate with a predetermined complexity threshold. Further, the AV management system can perform a downscaling of a neural network associated with an AV system based on the downscaling signal and determine a scenario to test the downscaled neural network in a simulation. The AV management system can adjust one or more parameters of the AV system based on simulated outputs and perform the simulation of the AV based on the adjusted one or more parameters of the AV system and the downscaled neural network to generate simulated performance data. Furthermore, the AV management system can compare the simulated performance data with a predetermined performance threshold.

Systems and methods for controlling a vehicle. The systems and methods receive static object detection data from a perception system. The static object detection data includes a first representation of a static object at a current time and a second representation of the static object at an earlier time. The systems and methods receive vehicle dynamics measurement data from the sensor system, determine a current position of the static object based on the first representation of the static object, predict an expected position of the static object at the current time using the second representation of the static object at the earlier time, a motion model and the vehicle dynamics measurement data, estimate a lateral velocity of the vehicle based on a disparity between the current position and the expected position, and control the vehicle using the lateral velocity