Systems and methods for dynamic predictive control of autonomous vehicles are disclosed. In one aspect, an in-vehicle control system for a semi-truck includes one or more control mechanisms configured to control movement of the semi-truck and a processor. The system further includes computer-readable memory in communication with the processor and having stored thereon computer-executable instructions to cause the processor to receive a desired trajectory and a vehicle status of the semi-truck, determine a dynamic model of the semi-truck based on the desired trajectory and the vehicle status, determine at least one quadratic program (QP) problem based on the dynamic model, generate at least one control command for controlling the semi-truck by solving the at least one QP problem, and provide the at least one control command to the one or more control mechanisms.

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.

Systems and methods are provided for controlling an autonomous vehicle. A method includes using a lateral controller system for determining a vehicle's curvature. A longitudinal controller system is used for determining desired vehicle acceleration. The longitudinal controller system uses a control loop with respect to a velocity error and a feedforward term. Commands are generated based on the output of the lateral controller system and the longitudinal controller system.

Autonomous driving simulation using recorded driving data is disclosed. A method of simulating autonomous driving includes receiving recorded driving data from a recorded autonomous vehicle (AV), the recorded driving data includes decision-making data generated using a first decision-making algorithm, sensing data, and movement data including positions of the recorded AV; obtaining simulation data from the recorded driving data by excluding the decision-making data from the recorded driving data; and simulating, by a simulation AV, a second decision-making algorithm using the simulation data. The simulating the second decision-making algorithm includes determining a first position of the simulation AV at a first time and adjusting a playback speed of the simulation data based on a difference between the first position and a second position of the positions of the recorded AV at the first time.

In one embodiment, an autonomous driving vehicle (ADV) speed following system determines how much and when to apply a throttle or a brake control of an ADV to maneuver the ADV around, or to avoid, obstacles of a planned route. The speed following system calculates a first torque force to accelerate the ADV, a second torque force to counteract frictional forces and wind resistances to maintain a reference speed, and a third torque force to minimize an initial difference and external disturbances thereafter between predefined target speed and actual speed of the ADV over a planned route. The speed following system determines a throttle-brake torque force based on the first, second, and third torque forces and utilizes the throttle-brake torque force to control a subsequent speed of the ADV.

A traveling work vehicle includes: a vehicle speed control portion configured to control the vehicle speed; a number-of-revolutions calculation portion configured to calculate, as an actual number of revolutions, a number of revolutions of a single rotary power source per unit time; a number-of-revolutions command generation portion configured to generate a number-of-revolutions command using a requested number of revolutions; a requested vehicle speed input portion configured to input a requested vehicle speed that is based on a user operation; a requested vehicle speed calculation portion configured to calculate a computed requested vehicle speed using a deviation between the requested number of revolutions and the actual number of revolutions, and the requested vehicle speed; and a vehicle speed command generation portion configured to give the vehicle speed control portion a vehicle speed command that is generated using the computed requested vehicle speed.

A device for controlling, in real time, the trajectory of an autonomous vehicle includes a control module which produces, in real time, from a state vector at each point in time, a first steering command in order to stabilize the trajectory of the vehicle relative to a vehicle path. The device includes an anticipation module which generates a variable representative of a meta-vector of deviations for each predicted position of the vehicle at points in time resulting in a given quantity at the current point in time and of the state vector at the current point in time. The control module produces the first steering command by quadratic optimization of a relationship between the generated representative variable and a meta-vector of successive steering commands for each predicted position of the vehicle at points in time resulting in a given quantity at the current point in time.

Systems, methods, and other embodiments described herein relate to adaptively selecting a controller for generating vehicle controls. In one embodiment, a method includes, in response to acquiring sensor data about a surrounding environment of the vehicle, determining a driving context of the vehicle in relation to aspects of a roadway on which the vehicle is traveling. The method includes selecting a controller for generating control inputs to the vehicle according to the driving context by selecting between a proportional, integral, derivative (PID) controller and a machine learning (ML) controller. The method includes controlling the vehicle using the controller.

Methods and systems are provided for increasing an efficiency of a modular hybrid transmission (MHT) of a hybrid vehicle. In one example, a method for operating an MHT comprises, at one or more control modules, determining an upper torque bound and a lower torque bound of a feedback controller based on a feedforward (FF) engine torque value, the feedback controller controlling a torque of an electric motor of the hybrid vehicle; and constraining operation of the electric motor via the feedback controller based on the upper and lower torque bounds. The electric motor may be controlled by a hybrid powertrain control module of the MHT. The upper and lower torque bounds may be calculated at a powertrain control module of the MHT based on torque converter losses.

An automotive vehicle control circuit can include a PID Controller that receives at an input a set point signal for the closed-loop control system and provides as an output a control signal that is fed to the motion control system. The PID controller is arranged in a closed-loop configuration with the motion control system to minimise an error value indicative of the difference between the demanded behaviour of the motion control system as indicated by the demand signal and the actual behaviour of the motion control system. The control circuit can include a neural network which has an input layer of neurons, at least one hidden layer of neurons, and an output layer comprising at least one output neuron, in which the neural network comprises a feedforward neural network that receives at the input layer of input neurons the demand signal, the drive signal output from the controller and the error value. The neural network is configured to determine one or more of the P gain, I gain and D gain terms used by the PID controller, and the neural network receives as a feedforward term at least one additional discrete environmental variable.