Dynamic braking capability of a combination vehicle including a tractor and at least one trailer is provided based on a distribution of the load carried by the combination vehicle. Load distribution is determined directly using load sensors disposed at wheel pairs of the tractor and trailer(s) or indirectly by using a load sensor located at the drive axle of the tractor together with engine torque and vehicle speed signals for determining gross vehicle mass. A database having sub-databases therein each storing stopping distance calculation results for a corresponding combination vehicle type e.g. 5-axle single or 8-axle double, is indexed by using the determined load distributions for providing the dynamic braking capability based on the vehicle type and its load distribution. The database may also be indexed using Axle Load Allocation Factor that is calculated based on a mathematical combination of drive, steering, and gross trailer axle loading.

A method includes an initial trailer health assessment and real-time trailer health monitoring. The initial trailer health assessment includes autonomous pre-trip maneuvers of the autonomous vehicle during a first time period, and detecting a pre-trip vehicle health condition. A vehicle health score is calculated based on the pre-trip vehicle health condition. If the vehicle health score is at least a threshold value, real-time trailer health monitoring is performed during a trip of the autonomous vehicle during a second time period, by actively monitoring vehicle dynamics data and/or image data associated with the autonomous vehicle, to determine a fault condition of the autonomous vehicle. If the fault condition meets a first criteria, a control parameter and/or a travel plan of the autonomous vehicle is adjusted. If the fault condition meets a second criteria different from the first criteria, a signal is sent to cause the autonomous vehicle to cease movement.

Aspects and implementations of the present disclosure relate, generally, to optimization of the autonomous vehicle technology and, more specifically, to a transfer hub for autonomous trucking operations. The transfer hub supports automated trailer delivery, trailer-tractor matching, trailer-tractor hitching, automated post-hitching visual inspection, automated refueling, parking spot calibration, load verification, load distribution assessment, automated brake inspection, and other improvements to the existing technology.

An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statutes for performing safe driving operation. Example embodiments relate to an autonomous vehicle having a trailer coupled to a rear thereof. An example method includes continuously predicting a trailer trajectory that is distinct from a planned trajectory of the autonomous vehicle. The method further includes determining that the predicted trailer trajectory is within a minimum avoidance distance away from a stationary vehicle located on a roadway on which the autonomous vehicle is located. The method further includes modifying the planned trajectory of the autonomous vehicle such that the predicted trailer trajectory satisfies the minimum avoidance distance. The method further includes causing the autonomous vehicle to navigate along the modified trajectory based on transmitting instructions to one or more subsystems of the autonomous vehicle.

An apparatus for controlling lane change is disclosed. The apparatus may include a weight detection unit sensing weight of freight, a safety space setting unit receiving sensed weight and setting a safety space required for a vehicle to safely move to a target lane from a current lane based on the sensed weight, a sensor unit sensing whether an object exists in the safety space using at least one sensor, and a control unit controlling the vehicle to move to the target lane within a preset time when there is no sensed object. A method of controlling lane change is also disclosed.

A control requirement determiner includes: a storage that stores at least one piece of location information and at least one piece of behavior information as past data in which the location information and the behavior information are associated with each other, the location information indicating a geographic location, the behavior information being related to a behavior exhibited by a vehicle body of a vehicle at the location indicated by the location information when the vehicle traveled in the past; and processing circuitry that determines, based on the behavior information associated with the location information of the past data, a control requirement to be imposed on a control target of a rough terrain vehicle at the location indicated by the location information of the past data during travel of the rough terrain vehicle.

An information processing method is executed by a computer and the method includes: obtaining first transported-object information that is transported-object information including a position and a weight of a transported object on a moving body; obtaining a weight of the moving body; generating information indicating a predicted steering angle center value by inputting the first transported-object information obtained and the weight of the moving body obtained into a model generated using second transported-object information, a weight of the moving body, and a steering angle center value of the moving body, the second transported-object information being older transported-object information than the first transported-object information; and outputting the information indicating the predicted steering angle center value to the moving body.

A parallel computing method for man-machine coordinated steering control of a smart vehicle based on risk assessment is provided, comprising the following steps: building a lateral kinetic equation model of a vehicle; building a target function by targeting at minimizing an offset distance of a vehicle driving track from a lane center line and making a change in a front wheel steering angle and a longitudinal acceleration as small as possible in a driving process; building a parallel computing architecture of a prediction model and the target function, and employing a triggering parallel computing method; solving and computing a gradient with a manner of back propagation and using a gradient descent method to obtain an optimal control amount of the front wheel steering angle and an optimal control amount of the longitudinal acceleration; and computing a driving weight, obtaining a desired front wheel steering angle and completing real time control.

A method and system for controlling clutch friction elements of an automatic transmission is provided. The method includes retrieving information about shift clutches from a data storage unit and acquiring information required to predict a temperature of a friction element for each shift clutch, deriving a predicted temperature value of a friction element for each shift clutch by using the information about the shift clutches and the information required to predict the temperature of the friction element, predicting whether or not overheating occurs for each shift clutch by comparing the derived predicted temperature value of the friction element for each shift clutch with an allowable temperature set for each shift clutch, and determining a target shift stage while avoiding the overheating clutch with a predicted temperature value exceeding the allowable temperature, through switching to an avoidance shift mode.

Disclosed are a method of improving fuel efficiency of a fuel cell electric vehicle, and an apparatus and a system therefor. The method includes collecting navigation information and vehicle speed information, calculating a coasting line when a specified event point is detected based on the navigation information, determining whether deceleration is necessary by comparing a current traveling speed with a coasting line speed corresponding to a current location, and changing a criterion for determining whether to enter a fuel cell stop (FC STOP) state when the deceleration is necessary as a determination result.