An apparatus of controlling regenerative braking for battery charging according to driving information may include a driving information generation device that generates the driving information of a vehicle, a controller that is configured to control regenerative braking of the vehicle according to the generated driving information, and a charging device that controls charging of a battery of the vehicle according to the controlled regenerative braking.

A method for scoring driving behavior using vehicle inputs and outputs is implemented in an electronic device. The method includes obtaining historical input data and output data of a vehicle; establishing an output regression model according to the historical output data; determining a boundary of the output regression model; establishing an input regression model according to the historical input data; determining a boundary of the input regression model by calculating boundary limits of the input regression model; obtaining real-time input data and output data of the vehicle; calculating a first ratio of data points outside the boundary of the input regression model to total data points in the real-time input data, and a second ratio of data points outside the boundary of the output regression model to total data points in the real-time output data; scoring driving behavior of a driver according to the first ratio and the second ratio.

A vehicle control apparatus includes a circuitry capable of controlling a vehicle. The circuitry is configured to estimate a traffic risk score, a travel risk score, and a driving ability score. The circuitry is configured to change a first threshold, with which automated vehicle attitude stability control is initiated, so as to promptly execute the automated vehicle attitude stability control when a travel risk is not avoided by the driving ability, change a second threshold, with which automatic entry avoidance control is initiated, so as to promptly execute the automatic entry avoidance control when the traffic risk is not avoided by the driving ability, and control the vehicle to travel on a target travel route when the traffic risk and the travel risk are not avoided by the driving ability or when the driving ability is lower than a specified level.

The present disclosure relates to a method and an apparatus for controlling an electronic control suspension using a deep learning-based road surface classification model. The method for controlling an electronic control suspension in a vehicle including a camera and a GPS receiver may include collecting location information of the vehicle using the GPS receiver while driving, identifying whether there is a previously generated road surface classification model corresponding to a front obstacle when the front obstacle is detected, determining a first control value based on a first characteristic value corresponding to the road surface classification model when there is the road surface classification model as a result of the identification, controlling the electronic control suspension with the determined first control value when entering the obstacle, and collecting new sensing data through a physical sensor, and correcting the first characteristic value based on the new sensing data.

A method controls a first vehicle in respect of an oncoming second vehicle. The method determines a turning situation of the first vehicle, in which an expected first trajectory of the first vehicle crosses an expected second trajectory of the second vehicle, and controls the first vehicle in such a way that, during the turning situation, a predetermined distance between the vehicles is maintained. The control includes an influencing of the direction of travel of the first vehicle.

The disclosure relates to the process of ascertaining an input variable of a vehicle actuator using a model-based predictive control. According to one exemplary arrangement, a processor unit is designed to access trajectory information and a state data set, which represents a state of surroundings of a vehicle and/or the state of the vehicle and/or a driving state of the vehicle, by an interface. The processor unit carries out a secondary condition algorithm in order to calculate a secondary condition and an MPC algorithm for a model-based predictive control. By carrying out the secondary condition algorithm, a secondary condition is ascertained for the MPC algorithm on the basis of the trajectory information and on the basis of the state data set. By carrying out the MPC algorithm, an input variable is ascertained for an actuator of the vehicle on the basis of the secondary condition. This is carried out in particular such that in a future predicted trajectory, the vehicle follows the specified trajectory with a specified degree of reliability.

The present disclosure provides a method and device for predicting a motion track of an obstacle and an autonomous vehicle, and relates to the technical field of autonomous driving, so as to at least solve the technical problem of low prediction precision of a motion track of an obstacle in an interaction scene. A specific implementation solution includes: environment information in a target scene, historical state information of a target obstacle and track planning information of a target vehicle are obtained, and the target obstacle is a potential interaction object of the target vehicle; and a motion track of the target obstacle is predicted based on the environment information, the historical state information and the track planning information.

Techniques are described that enable automatic emergency braking (AEB) for a path-crossing target when a collision between a host vehicle and the target that is deemed imminent. Based on whether an acceleration of the host vehicle is above a threshold. Based on the acceleration, and, optionally, a location of the target relative to a crossing path (e.g., whether a portion of the target is within a suppression zone), an AEB system of the host vehicle is either activated or not activated, for example, suppressed. This suppression of the AEB system may include gating or nulling an AEB activation signal to prevent an emergency braking event. By managing the AEB system in a path-crossing scenario, many common false-positive AEB events (warnings, alerts, and/or braking) may be avoided. Furthermore, intentional vehicle maneuvers that comply with normal driving etiquette or rules can still be allowed for operator and passenger comfort, without risking safety.

Systems providing a comparison of results of simulations of operation of a simulated autonomous vehicle may include a processor to: perform a first simulation of operation of a simulated autonomous vehicle based on first autonomous vehicle control code, receive second autonomous vehicle control code that includes a second version of software code associated with controlling operations of the simulated autonomous vehicle, the second version including a modification of a first version of software code associated with controlling operations of the simulated autonomous vehicle, perform a second simulation of operation of the simulated autonomous vehicle based on the second autonomous vehicle control code, and display an indication that second values of one or more metrics that resulted from the second simulation are different from one or more first values of the one or more metrics that resulted from the first simulation. Methods, computer program products, and autonomous vehicles are also disclosed.

Disclosed are a vehicle capable of controlling acceleration in consideration of a driving environment and an acceleration limit control method therefor. The acceleration limit control method of the disclosure includes determining a base value of an acceleration limit level, which is classified into a plurality of levels, determining a first correction value based on manipulation of an accelerator pedal, determining a second correction value based on a driving environment, determining a final acceleration limit level by applying the first correction value and the second correction value to the base value, and determining a limit acceleration based on the final acceleration limit level.