Methods and systems are provided for controlling vehicle torque output during cruise control. In one example, a method may include determining future vehicle torque output by minimizing an objective function based on an instantaneous vehicle speed, an average vehicle speed, and a present vehicle torque output. The weights of the objective function may be updated based on the past and the present vehicle operating parameters.

A system for controlling a vehicle a sensor to sense measurements indicative of a state of the vehicle and a memory to store a motion model of the vehicle, a measurement model of the vehicle, and a mean and a variance of a probabilistic distribution of a state of calibration of the sensor. The motion model of the vehicle defines the motion of the vehicle from a previous state to a current state subject to disturbance caused by an uncertainty of the state of calibration of the sensor in the motion of the vehicle. The measurement model relates the measurements of the sensor to the state of the vehicle using the state of calibration of the sensor. The system includes a processor to update the probabilistic distribution of the state of calibration based on a function of the sampled states of calibration weighted with weights determined based on a difference between the state of calibration sampled on a feasible space defined by the probabilistic distribution and the corresponding state of calibration estimated based on the measurements using the motion and the measurements models. The system includes a controller to control the vehicle using the measurements of the sensor adapted using the updated probabilistic distribution of the state of calibration of the sensor.

Systems and methods to control recapture and use of energy to provide an APU include a vehicle having an electrically powered drive axle to provide supplemental torque to the vehicle to supplement primary motive forces applied through a separate drivetrain powered by a fuel-fed engine of the vehicle. The vehicle further includes an energy store to supply the electrically powered drive axle with electrical power or receive energy recovered using the electrically powered drive axle. The vehicle also includes the APU coupled to receive electrical power from the energy store for stopover operation and without idling of the fuel-fed engine. Further, the vehicle includes a hybrid control system for managing, based on an estimated travel time to a stopover location, an SoC of the energy store while the vehicle travels over a roadway to provide a target SoC of the energy store when the vehicle arrives at the stopover location.

Technologies for efficiently providing reliable compute operations for mission critical applications include a reliability management system. The reliability management system includes circuitry configured to obtain conclusion data indicative of a conclusion made by each of two or fewer compute devices of a host system. The conclusion data from each compute device pertains to the same operation. Additionally, the circuitry is configured to identify whether an error has occurred in the operation of each compute device, determine, in response to a determination that an error has occurred, a severity of the error, and cause the host system to perform a responsive action as a function of the determined severity of the error.

The embodiments of the present application provide a method and an apparatus for predicting an average energy consumption of an electric vehicle. The method comprises: according to a real-time voltage and a real-time current of a battery pack of an electric vehicle, determining an actual energy consumption of the electric vehicle in the traveled mileage segment corresponding to the current time, the traveled mileage segment comprising a plurality of unit mileage segments; according to the actual energy consumption of each unit mileage segment of the traveled mileage segment, determining an initial average energy consumption of the electric vehicle at the current time; acquiring a target average energy consumption and average energy consumption adjustment parameters of the electric vehicle; and according to the initial average energy consumption, the target average energy consumption and the average energy consumption adjustment parameters, determining the actual average energy consumption of the electric vehicle at the current time. The present application can accurately calculate the actual average energy consumption of the electric vehicle at the current time in real time.

A system for expanding the operational design domain (ODD) of an autonomous agent includes a decision-making platform (equivalently referred to herein as a decision-making architecture). A method for expanding the operational design domain (ODD) includes determining a decision-making architecture for a first domain and adapting the decision-making architecture to a second domain. Additionally or alternatively, the method 200 can include implementing the decision-making architecture S300 and/or any other processes.

A path generation device and a vehicle control system capable of inhibiting generation of paths that make a vehicle unstable are provided. The path generation device includes a path generator that generates a plurality of paths along which a vehicle is to travel, in association with each piece of environment measurement data about a travel environment of the vehicle detected by a plurality of detectors; a reliability setting part that sets, for each of the generated paths, the reliability of the path in itself, the reliability corresponding to the degree to which a variation in the path falls within a predetermined range, and a path-weight setting part that sets the weight of each path on the basis of the reliability of the path.

Methods and systems are provided for detecting faults in a sensor and reconstructing an output signal without use of the faulty sensor. In one embodiment, a method includes: receiving, by a processor, sensor data indicating a measured value from a first sensor; receiving, by a processor, sensor data indicating measured values from a plurality of other sensors; computing, by a processor, virtual values based on a vehicle model and the sensor data from the plurality of other sensors; computing, by a processor, a residual difference between the measured value from the first sensor and the virtual values; detecting, by a processor, whether a fault exists in the first sensor based on the residual difference; and when a fault in the sensor is detected, generating, by a processor, a control value based on the virtual values instead of the measured value.

Disclosed are a vehicle control apparatus and a vehicle control method. The vehicle control apparatus according to an embodiment of the present disclosure includes an input unit to receive current lane information, current traffic sign information, and current driver behavior information photographed by a photographing apparatus, a determination unit to determine whether a vehicle is in an inappropriate driving pattern based on the inputted current lane information and the current traffic sign information and to determine whether a driver is in a driver carelessness state based on the inputted current driver behavior information, and a controller to transmit a warning command to a warning apparatus so that the warning apparatus warns the driver that the driver is currently in a careless driving state if the vehicle is in the inappropriate driving pattern and the driver is in the driver carelessness state.

A system and method for adaptive cruise control for defensive driving are disclosed. A particular embodiment includes: receiving input object data from a subsystem of an autonomous vehicle, the input object data including distance data and velocity data relative to a lead vehicle; generating a weighted distance differential corresponding to a weighted difference between an actual distance between the autonomous vehicle and the lead vehicle and a desired distance between the autonomous vehicle and the lead vehicle; generating a weighted velocity differential corresponding to a weighted difference between a velocity of the autonomous vehicle and a velocity of the lead vehicle; combining the weighted distance differential and the weighted velocity differential with the velocity of the lead vehicle to produce a velocity command for the autonomous vehicle; and controlling the autonomous vehicle to conform to the velocity command.