Abstract: A method, a device, and a system for parking a truck accurately in a shore crane area are provided. A vehicle controller transmits a parking request for a truck to be parked. A main controller receives the parking request and acquires real-time point cloud data by scanning one or more lanes crossed by a shore crane using one or more LiDARs. The main controller clusters the real-time point cloud data to obtain a set of point clouds for the truck and applies an Iterative Closest Point algorithm to the set of point clouds and a vehicle point cloud model to obtain a real-time distance from the truck to a target parking space. The vehicle controller controls the truck to stop at the target parking space based on the real-time distance.

Abstract: A control system for a trailer or dolly, wherein the trailer or dolly comprises a perception sensor which is directed in a first travelling direction of the trailer or dolly and a coupling member for coupling with a vehicle further ahead in the first travelling direction, wherein the control system is configured to provide and/or use data from the sensor for a first control mode when the trailer or dolly is not coupled to the vehicle via the coupling member and to provide and/or use data from the sensor for a second control mode different from the first control mode when the trailer or dolly is coupled to the vehicle via the coupling member.

Abstract: The application relates to a lateral motion control method and system for a vehicle, an automatic driving controller, a steering system, a vehicle, and a storage medium, wherein the lateral motion control method for the vehicle comprises: generating and outputting a steering control command according to a desired track of the vehicle, such that the vehicle moves along an actual track according to the steering control command; determining a vehicle running state, wherein the vehicle running state is generated according to an error value between the desired track and the actual track; and determining whether to stop/limit outputting of the steering control command or not according to the vehicle running state. The lateral motion of the vehicle can be controlled according to the method.

Abstract: This application provides an internet of things platoon communication method. In this method, an internet of things platform plans and manages a communication mode of a platoon in a traveling process. In the traveling process, the platoon selects, based on a communication mode plan sent by the platform, a communication mode corresponding to a current location of the platoon for communication. Because the platform can obtain global information such as a network and a map, the communication mode planned by the platform is more reasonable and accurate. This resolves a problem of one-by-one switchover and repeated switchover of fleet members at road sections such as a congested road section, a road section with poor network coverage, and a multi-block road section.

Abstract: An anti-collision method includes storing information on a current position of at least one vehicle within a target work volume, scanning the target work volume by a radiofrequency radiation signal to detect the presence of at least one human operator within the target work volume, determining the current position of the at least one human operator within the target work volume, comparing the current position of the at least one human operator with the current position of the at least one vehicle, and, if the current position of the at least one human operator at least partially overlaps the current position of the at least one vehicle, activating at least one safety device.

Abstract: A vehicle control device performs learning to correct a parameter used in a control program for controlling a vehicle. The vehicle control device stores learning data obtained by the learning in a storage unit. The vehicle control device determines whether or not a part controlled by the parameter has been replaced, and resets the learning data stored in the storage unit when it is determined that the part has been replaced. The vehicle control device determines that the part has been replaced based on an adjustment that is performed upon replacement of the part. The vehicle control device quickly mitigates degradation of the controllability of the vehicle after replacement of the part.

Abstract: A device is provided for use with a vehicle and with a communication device. The communication device can transmit a first vehicle mode signal and a subsequent signal. The device includes a processing component, an indicator component, a transmitting component and a receiving component. The processing component can operate in a vehicle mode and can operate in a second mode. The indicator component can provide a vehicle mode indication signal when the processing component is operating in the vehicle mode. The transmitting component can transmit a second vehicle mode signal based on the vehicle mode indication signal. The receiving component can receive the first vehicle mode signal and can receive the subsequent signal. The processing component can further perform a function while in the vehicle mode and based on the first vehicle mode signal and the subsequent signal.

Abstract: A method of controlling tethered self-propelled platforms is provided. The method comprises providing a platform leader and a platform follower connected to the leader with a tether to define a first heading line of the leader and a first coordinate frame of the follower. Each of the leader and the follower is pivotally moveable relative to the tether, defining a leader angle and a follower angle. The method further comprises estimating a predicted position of the leader based on a current position, a current speed, and a current yaw rate of the leader. The predicted position of the leader defines a predicted heading line of the leader. The method further comprises determining a trajectory of the follower from the first coordinate frame to the predicted heading line defining a second coordinate frame of the follower. The trajectory is based on a desired distance the predicted heading line and a desired change in yaw angle of the follower.

Abstract: In a method for monitoring the operational reliability of a braking system in a vehicle. A brake pedal actuation is monitored via a brake light switch. An error signal is generated if the brake light switch generates a switch signal, without a braking intent of the driver being registered via a sensor signal of a sensor.

Abstract: An agricultural machine includes a vehicle body including a front wheel and a rear wheel, an obstacle detector to detect an obstacle, and a position changer to change a position of the obstacle detector. The position changer moves the obstacle detector between a detection position that is a predetermined position to detect an obstacle and an evacuation position to evacuate from the detection position toward the vehicle body side. The position changer moves the obstacle detector to various detection positions allowing detection of the obstacle.

Abstract: Disclosed is a mobile robot including a driver configured to move a main body, an image obtainer configured to obtain an image of the surroundings and far and near distances between the main body and an obstacle, and a controller configured to analyze the image and the far and near distances obtained by the image obtainer and to determine whether an obstacle is present near the main body. The controller determines whether the obstacle is a mat-type obstacle. Upon determining that the obstacle is a mat-type obstacle, the controller calculates the planar area of the mat-type obstacle, and determines a climbing operation or an avoidance operation of the main body based on the planar area of the mat-type obstacle.

Abstract: Techniques are discussed for generating and optimizing trajectories for controlling autonomous vehicles in performing on-route and off-route actions within a driving environment. A planning component of an autonomous vehicle can receive or generate time-discretized (or temporal) trajectories for the autonomous vehicle to traverse an environment. Trajectories can be optimized, for example, based on the lateral and longitudinal dynamics of the vehicle, using loss functions and/or costs. In some examples, the temporal optimization of a trajectory may include resampling a previous trajectory based on the differences in the time sequences of the temporal trajectories, to ensure temporal consistency of trajectories across planning cycles. Constraints also may be applied during temporal optimization in some examples, to control or restrict driving maneuvers that are not supported by the autonomous vehicle.

Abstract: Various technologies described herein pertain to routing autonomous vehicles based upon spatiotemporal factors. A computing system receives an origin location and a destination location of an autonomous vehicle. The computing system identifies a route for the autonomous vehicle to follow from the origin location to the destination location based upon output of a spatiotemporal statistical model. The spatiotemporal statistical model is generated based upon historical data from autonomous vehicles when the autonomous vehicles undergo operation-influencing events. The spatiotemporal statistical model takes, as input, a location, a time, and a direction of travel of the autonomous vehicle. The spatiotemporal statistical model outputs a score that is indicative of a likelihood that the autonomous vehicle will undergo an operation-influencing event due to the autonomous vehicle encountering a spatiotemporal factor along a candidate route.

Abstract: In some examples, a first electronic control unit (ECU) receives zone sensor data of a plurality of zones associated with a vehicle. For instance, a respective second ECU and a respective set of zone sensors is associated with each respective zone of the plurality of zones. Based on an indicated driving mode of the vehicle, the first ECU may perform recognition on the zone sensor data from a first zone of the plurality of zones to determine first recognition information. The first ECU receives second recognition information from the respective second ECUs of the respective zones. For instance, the respective second ECUs are configured to perform recognition processing on respective zone sensor data from the set of zone sensors of the respective zones. Based on the first recognition information and the second recognition information, the first ECU sends at least one control signal to at least one vehicle actuator.

Abstract: Evasive steering assist (ESA) systems and methods for a vehicle utilize a set of vehicle perception systems configured to detect an object in a path of the vehicle, a driver interface configured to receive steering input from a driver of the vehicle via a steering system of the vehicle, a set of steering sensors configured to measure a set of steering parameters, and a controller configured to determine a set of driver-specific threshold values for the set of steering parameters, compare the measured set of steering parameters and the set of driver-specific threshold values to determine whether to engage/enable an ESA feature of the vehicle, and in response to engaging/enabling the ESA feature of the vehicle, command the steering system to assist the driver in avoiding a collision with the detected object.

Abstract: A vehicle data collection system includes a vehicle-mounted sensor; a non-transitory computer readable medium configured to store instructions; and a processor connected to the non-transitory computer readable medium. The processor is configured to execute the instructions for generating an invariant feature map, using a first neural network; and comparing the invariant feature map to template data to determine a similarity between the invariant feature map and the template data, wherein the template data is received from a server. The processor is further configured to execute the instructions for determining whether the determined similarity is exceeds a predetermined threshold; and instructing a transmitter to send the sensor data to the server in response to a determination that the determined similarity exceeds the predetermined threshold.

Abstract: Techniques for determining a prediction probability associated with a disengagement event are discussed herein. A first prediction probability can include a probability that a safety driver associated with a vehicle (such as an autonomous vehicle) may assume control over the vehicle. A second prediction probability can include a probability that an object in an environment is associated the disengagement event. Sensor data can be captured and represented as a top-down representation of the environment. The top-down representation can be input to a machine learned model trained to output prediction probabilities associated with a disengagement event. The vehicle can be controlled based the prediction probability and/or the interacting object probability.

Abstract: A server apparatus includes a communication unit, and a control unit configured to send an instruction for causing flight vehicles to perform a flight operation, to the flight vehicles. The flight operation includes energizing, by a first flight vehicle, a penetration tool toward a target object in a space, holding a first part, penetrated through the target object, and a second part, not penetrated through the target object, of a cord-shaped member attached to the penetration tool, and flying to outside the space, waiting, by a second flight vehicle, outside the space and receiving any one of the first and second parts from the first flight vehicle, and towing, by the first and second flight vehicles, the target object with the cord-shaped member and transporting the target object to outside the space by flying while respectively holding any one and the other of the first and second parts.

Abstract: A method for determining information on an expected trajectory of an object comprises: determining input data being related to the expected trajectory of the object; determining first intermediate data based on the input data using a machine-learning method; determining second intermediate data based on the input data using a model-based method; and determining the information on the expected trajectory of the object based on the first intermediate data and based on the second intermediate data.

Abstract: One variation of a method for customizing motion characteristics of an autonomous vehicle for a user includes: accessing a baseline emotional state of the user following entry of the user into the autonomous vehicle at a first time proximal a start of a trip; during a first segment of the trip, autonomously navigating toward a destination location according to a first motion planning parameter, accessing a second emotional state of the user at a second time, detecting degradation of sentiment of the user based on differences between the baseline and second emotional states; and correlating degradation of sentiment of the user with a navigational characteristic of the autonomous vehicle; modifying the first motion planning parameter of the autonomous vehicle to deviate from the navigational characteristic; and, during a second segment of the trip, autonomously navigating toward the destination location according to the revised motion planning parameter.