In a drive assist system, a map data acquiring section acquires at least one of a driver's operation ability and a load of a vehicle. An adjustment section determines an assist control amount as a control parameter of drive assist for the vehicle so that a degree of the driver's operation is increased according to reduction of the driver's operation ability or increasing of the load of the vehicle. An assist control amount calculation section transmits the assist control amount to a steering motor and a notification section so as to execute the drive assist for the vehicle.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

A method for reconstructing a motion track applied to a terminal is provided. The method includes: obtaining a data set, the data set including positioning data obtained by positioning a target object; performing data fitting on target data in the data set to obtain a plurality of segments of first curves, the target data being positioning data obtained by performing noise filtering on the data set; and determining a motion track of the target object based on the plurality of segments of first curves. Counterpart apparatus and non-transitory computer-readable storage medium embodiments are also contemplated.

A vehicle driving assist device comprises a risk determination region setting unit configured to set a risk determination region for calculating a risk degree that decreases as a distance outward from a center of the oncoming moving body in a width direction of the oncoming moving body increases. The risk determination region setting unit is further configured to set the risk determination region such that the risk degree is zero at a distance in a vehicle width direction between the vehicle and the oncoming moving body matching an average distance. The average distance is calculated based on distances in the vehicle width direction between the vehicle and oncoming moving bodies acquired every time the vehicle passes by the oncoming moving bodies.

A method, apparatus, and system for planning a deceleration trajectory based on a natural deceleration profile for an autonomous driving vehicle (ADV) is disclosed. In one embodiment, in response to a request for driving in a natural deceleration mode, a current speed of the ADV is determined. A speed guideline is generated based on a predetermined natural deceleration profile associated with the ADV in view of the current speed of the ADV. A speed planning operation is performed by optimizing a total cost function based on the speed guideline to determine speeds of a plurality of trajectory points along a trajectory planned to drive the ADV. A number of control commands are generated based on the speed planning operation to control the ADV with the planned speeds along the trajectory, such that the ADV naturally slows down according to the predetermined natural deceleration profile.

A control method controls an electric four-wheel-drive vehicle to switch a drive torque distribution between a first distribution prioritizing energy efficiency and a second distribution prioritizing driving performance. The distribution is set to the second distribution where wheel slip is detected during a trip, and returned to the first distribution once the vehicle has stopped. When wheel slip is detected at least during acceleration, the distribution is switched from the first distribution to the second distribution. When wheel slip is detected during deceleration, a slip experience flag is set. The slip experience flag is maintained at least until starting off in a subsequent trip. Where the slip experience flag has been set, the distribution is maintained as the second distribution when the vehicle has stopped, and where the slip experience flag has not been set, the distribution is returned to the first distribution upon the vehicle being stopped.

Controlling an actual slip of at least one driven axle of a motor vehicle with at least one axle having at least one wheel and a one drive unit for providing a drive torque for the axle and for the wheel can be carried out by a control device for controlling the drive unit. The control device can be configured for establishing a first actual speed of the motor vehicle; establishing a second actual speed of the at least one wheel; calculating a target speed of the at least one wheel for the established first actual speed taking into account parameters; determining an actual slip of the at least one wheel with respect to a substrate on which the motor vehicle is being moved; when the actual slip exceeds a defined first limit slip, generating a limit torque by which the drive torque produced by the drive unit is adjusted.

A cruise control method and system, and a vehicle, the method being applied to a first vehicle. When the first vehicle is driving in a first lane, before the first vehicle enters from the first lane into a preset recognition region to thereby complete vehicle identity recognition in the preset recognition region, the cut-in probability of a second vehicle cutting into the first lane can be predicted according to a movement parameter of the second vehicle that is in an adjacent second lane, and it is determined, according to the cut-in probability and the stopping time of the first vehicle, whether to brake to stop the first vehicle; therefore, during the process of controlling the first vehicle to pass through the preset recognition region, the driver is not required to temporarily take over control of the first vehicle.

A method for operating an automated motor vehicle during automated driving includes determining the current motor vehicle route data, a current trajectory of the motor vehicle, and environment information. The method also includes ascertaining a driving maneuver that is to be expected, associating this driving maneuver with one of the at least two motoring comfort levels, and outputting advice to the occupants of the motor vehicle if the current motoring comfort level changes as a result. The method further includes automatically and actively taking measures relating to the interior to adjust the interior to suit the motoring comfort level that is to be expected.

There is provided a method for preventing car collision, the method comprising: obtaining information about a vehicle stopped in front of a preceding vehicle acquired by the preceding vehicle; calculating a deceleration amount for preventing a collision with the stopped vehicle based on the information about the stopped vehicle; predicting a preset event related to the preceding vehicle based on a change in the position of the preceding vehicle; and controlling an autonomous driving vehicle based on the deceleration amount according to a prediction result.