A method for automated guidance of a vehicle along a specified setpoint trajectory at an actual speed, wherein the setpoint trajectory includes geometric trajectory variables, the method including ascertaining an actual deviation of the vehicle from the setpoint trajectory and outputting the ascertained actual deviation and generating manipulated variables such that the vehicle, in the case of automated control of a drive system and/or a braking system and/or a steering system of the vehicle, moves closer to the setpoint trajectory. The method also includes ascertaining whether an undesirable driving state is present when the vehicle moves closer to the setpoint trajectory, wherein the undesirable driving state is ascertained from the specified setpoint trajectory based on the geometric trajectory variable, and, if the presence of the undesirable driving state is identified, automatically controlling the vehicle based on generated stability variables and/or adapting the setpoint trajectory.

The present disclosure relates to an apparatus for automatically locking a container, which includes: a cylindrical housing: a top plate installed on the top of the housing; a bottom plate installed under the housing; an elevation unit installed to move up and down in the housing; a locker installed to rotate left and right to lock and unlock a container; a guide guiding up-down movement and rotation movement of the locker; a first elastic supporting member elastically supporting upward the elevation unit; and a second elastic supporting member elastically supporting upward the locker. Accordingly, the present disclosure provides an effect that a container is automatically locked or unlocked by its own weight when it is loaded or unloaded, so it is possible to safely transport heavy containers always in a locked state and it is possible to prevent malfunction or poor function of the locker.

A vehicle operable to pull a trailer comprising a payload is provided, that includes a plurality of sensors configured to capture sensor data related to the vehicle, the trailer, or both, and a controller configured to (i) receive the sensor data from the plurality of sensors, (ii) determine, based on the sensor data, one or more parameters associated with the trailer, the payload, or both, (iii) update, based on an analysis of the one or more parameters, a confidence level associated with an operation of the vehicle with the trailer and the payload, and (iv) based on the confidence level, responsively execute an autonomous control strategy comprising one or more adjustments to the operation of the vehicle.

Systems and methods for general driving behavior of an autonomous vehicle are disclosed. In one aspect, an autonomous vehicle includes a trailer, at least one perception sensor, a non-transitory computer readable medium, and a processor. The processor is configured to estimate a grade of the roadway based on the perception data, provide a first control input to the autonomous vehicle based on the grade of the roadway, determine a response of the autonomous vehicle to the first control input based on the perception data, estimate a trailer load of the trailer based on the response of the autonomous vehicle to the first control input, and provide a second control input to the autonomous vehicle based on the grade of the roadway and the trailer load.

A high precision digital map is pre-developed and stored in a memory of an in-vehicle control computer on an autonomous vehicle. The digital map is updated by the in-vehicle control computer with detected roadway data that is a fusion of roadway perception data from at least one perception sensor on the autonomous vehicle and real time GPS signal from at least one GPS receiving devices on the autonomous vehicle. The updated digital map is transferred to a remote oversight system via a network communication subsystem, and the oversight system distributes the updated digital map to other autonomous vehicles connected over the network communication subsystem.

Systems and methods for detecting physical infrastructure related to navigation of an autonomous vehicle are disclosed. In one aspect, the autonomous vehicle includes a perception sensor configured to generate perception data, a non-transitory computer readable medium, and a processor. The processor is configured to determine a minimal risk condition (MRC) maneuver for the autonomous vehicle to execute, identify a safe zone in which the autonomous vehicle is able to execute the MRC maneuver by coming to a stop based on the perception data, identify one or more exclusion zones within the safe zone based on the perception data, and control the autonomous vehicle to execute the MRC maneuver including stopping outside of the exclusion zone.

Techniques are described to determine parameters and/or values for a control model that can be used to operate an autonomous vehicle, such as an autonomous semi-trailer truck. For example, a method of obtaining a data-driven model for autonomous driving may include obtaining data associated with a first set of variables that characterize movements of an autonomous vehicle over time and commands provided to the autonomous vehicle over time, determining, using at least the first set of data, non-zero values and an associated second set of variables that describe a control model used to perform an autonomous driving operation of the autonomous vehicle, and calculating values for a feedback controller that describes a transfer function used to perform the autonomous driving operation of the autonomous vehicle driven on a road.

Systems and methods for managing environmental conditions for an autonomous vehicle are disclosed. In one aspect, an autonomous vehicle includes a perception sensor configured to generate perception data indicative of a condition of the environment, a network communication transceiver configured to communicate with an oversight system and an external weather condition source, a non-transitory computer readable medium, and a processor. The processor is configured to: receive the perception data from the at least one perception sensor, receive an indication of current weather conditions from the external weather condition source, determine a current environmental condition severity level from a plurality of severity levels based on the perception data and the indication of current weather conditions, modify one or more driving parameters that that govern a range of actions that can be autonomously executed by the autonomous vehicle, and navigate the autonomous vehicle based on the modified driving parameters.

Systems and methods for situational behavior of an autonomous vehicle are disclosed. In one aspect, an autonomous vehicle includes at least one perception sensor configured to generate perception data indicative of at least one other vehicle on a roadway, a non-transitory computer readable medium, and a processor. The processor is configured to determine that the other vehicle is violating one or more rules of the roadway based on the perception data, tag the other vehicle as a non-compliant driver, and modify control of the autonomous vehicle in response to tagging the other vehicle as a non-compliant driver.

A control device associated with an autonomous vehicle receives sensor data and detects that the autonomous vehicle is approaching a railroad crossing based on the sensor data. The control device determines a target lane to travel while crossing the railroad. The target lane is a lane that has available space with at least a length of the autonomous vehicle on the other side of the railroad as opposed to a side of the railroad where the autonomous vehicle is currently traveling. The control device instructs the autonomous vehicle to travel on the target lane. The control device determines that no train is approaching the railroad crossing. The control device instructs the autonomous vehicle to cross the railroad if the target lane still provides available space with at least the length of the autonomous vehicle on the other side of the railroad.