The disclosure relates to a vehicle velocity control method. The method includes: determining, by an onboard sensor, drivable distances in different directions in front of a current vehicle, and obtaining, at least based on types of targets in the different directions, an area of a drivable space in front of the current vehicle; determining, based on the area of the drivable space and a current vehicle velocity, a result of a safety degree in a current driving scenario; and controlling the vehicle velocity of the current vehicle based on the result of the safety degree. The disclosure further relates to a vehicle control device, a computer storage medium, and a vehicle.

In an overhead-structure recognition device to be mounted to a vehicle, a determination unit is configured to, in response to a vertical distance between an object of interest and a high-reflectivity object being greater than or equal to a predefined value of vertical distance, determine that the object of interest is an overhead structure which is a structure located above the vehicle that does not obstruct travel of the vehicle. The object of interest corresponds to a subset of interest among a plurality of subsets acquired by dividing range point cloud data. The high-reflectivity object is an object other than the object of interest, among objects corresponding to the respective subgroups, whose reflectance is greater than or equal to a predefined value of reflectance.

An arrangement determines a gradient signal in a vehicle. The arrangement has: a position capture device that determines a vehicle position at a first and second time and ascertains therefrom a distance traveled as a motion vector, and a laser distance sensor on the vehicle front at an angle to a vehicle longitudinal axis and configured to emit a laser beam in a direction of a first measuring point in front of the vehicle at the first and second time, and a length sensor to ascertain the length of the laser beam and its associated vector at the first and second time, and at least one detection device to ascertain a differential vector from the motion vector and the ascertained vectors and to form a gradient signal therefrom.

The present invention obtains a processor, a processing method, a warning system, and a straddle-type vehicle capable of improving both the rider's safety and the rider's comfort. A processor (20) includes: an acquisition section that acquires surrounding environment information corresponding to output of a surrounding environment detector (11) during travel of a straddle-type vehicle (100); a determination section that determines necessity of warning operation provided to the rider and generated by the warning system (1); and a control section that makes an alarm (30) perform the warning operation in the case where the determination section determines that the warning operation is necessary. The acquisition section further acquires helmet posture direction information corresponding to output of a helmet posture direction detector (13) during the travel of the straddle-type vehicle (100). The determination section determines the necessity of the warning operation on the basis of the surrounding environment information and the helmet posture direction information.

A LIDAR system includes a plurality of LIDAR units. Each of the LIDAR units includes a housing defining a cavity. Each of the LIDAR units further includes a plurality of emitters disposed within the cavity. Each of the plurality of emitters is configured to emit a laser beam. The LIDAR system includes a rotating mirror and a retarder. The retarder is configurable in at least a first mode and a second mode to control a polarization state of a plurality of laser beams emitted from each of the plurality of LIDAR units. The LIDAR system includes a polarizing beam splitter positioned relative to the retarder such that the polarizing beam splitter receives a plurality of laser beams exiting the retarder. The polarizing beam is configured to transmit or reflect the plurality of laser beams exiting the retarder based on the polarization state of the laser beams exiting the retarder.

The present disclosure describes a computer-implemented method for controlling a vehicle. In aspects, the computer-implemented method includes acquiring sensor data from a sensor, determining first processed data related to a first area around the vehicle based on the sensor data using a machine-learning method, and determining second processed data related to a second area around the vehicle based on the sensor data using a conventional method. The second area may include a subarea of the first area. In addition, the computer-implemented method includes controlling the vehicle based on the first processed data and the second processed data.

The present invention relates generally to the field of lane-keeping devices. More specifically, the present invention relates to a device that is primarily comprised of a housing, further comprised of at least one button, at least one laser emitter, a battery, and a light sensor. Further, the device is comprised of a bottom wall, further comprised of at least one fastener. The device is also comprised of a top wall that may be comprised of a solar panel to power the device. Further, the device, via laser emitter, emits a light beam directed out in front of the vehicle onto the roadway. The light beam shown on the roadway allows the driver of the vehicle to know precisely where in the lane their vehicle is located. The device further allows for the light emitter to be changed to encompass an array of different intensities and distances.

A vehicle includes one or more laser scanners and an on-board vehicle computer system communicatively coupled to the laser scanners. The computer system uses information (e.g., coordinate points) obtained from the laser scanners to calculate a trailer angle (e.g., a cab-trailer angle) for the vehicle. The computer system may include a shape detection module that detects a trailer based on the information obtained from the laser scanners and an angle detection module that calculates an angle of the detected trailer relative to a laser scanner, calculates the orientation of the detected trailer based on that angle and dimensions (e.g., width and length) of the trailer, and calculates a cab-trailer angle based on the orientation of the trailer. The computer system may include an autonomous operation module configured to use the cab-trailer angle in an autonomous or computer-guided vehicle maneuver, such as a parking maneuver or backing maneuver.

A method and system for creating a map of an environment surrounding a vehicle includes a camera for obtaining images including objects within an environment from at least one camera mounted on the vehicle and a controller configured to create a depth map of the environment based on the images and vehicle odometry information. A laser scan of the depth map is created and used to create a two-dimensional map utilized for operating the vehicle.

A system and method for selectively reducing or filtering data provided by one or more vehicle mounted sensors before using that data to detect, track and/or estimate a stationary object located along the side of a road, such as a guardrail or barrier. According to one example, the method reduces the amount of data by consolidating, classifying and pre-sorting data points from several forward looking radar sensors before using those data points to determine if a stationary roadside object is present. If the method determines that a stationary roadside object is present, then the reduced or filtered data points can be applied to a data fitting algorithm in order to estimate the size, shape and/or other parameters of the object. In one example, the output of the present method is provided to automated or autonomous driving systems.