An automated steering device is provided usable in conjunction with power equipment machines. By way of example, the automated steering device can provide user-assisted steering for a power equipment machine to maintain tight parallel paths. The user-assisted steering can be defined relative to an initial vector traversed through user directed operation of the power equipment machine, independent of or at least in part independent of a predefined area of operation for the power equipment machine. Position location data refined by local terrestrial positioning system correction devices, or onboard rotational correction devices can be provided to obtain high positioning accuracy, and minimal path deviation. As a result, highly accurate pathing can be provided by way of the disclosed automated steering devices.

A traffic radar system comprises a first radar transceiver, a second radar transceiver, a speed determining element, and a processing element. The first radar transceiver transmits and receives radar beams and generates a first electronic signal corresponding to the received radar beam. The second radar transceiver transmits and receives radar beams and generates a second electronic signal corresponding to the received radar beam. The speed determining element determines and outputs a speed of the patrol vehicle. The processing element is configured to receive a plurality of digital data samples derived from the first or second electronic signals, receive the speed of the patrol vehicle, process the digital data samples to determine a relative speed of at least one target vehicle in the front zone or the rear zone, and convert the relative speed of the target vehicle to an absolute speed using the speed of the patrol vehicle.

In order to realize a device that can reduce the influence of errors, the device includes a first acquisition unit configured to acquire first information including an error via an image formation optical system, a second acquisition unit configured to acquire second information of which the error is less than that of the first information, a correction information generation unit configured to calculate a correction value for correcting the error of the first information on the basis of the second information, and a correction unit configured to correct the first information by using the correction value.

To provide a radar apparatus which can prevent occurrence of two or more of calculated values of the actual relative speeds by round of combination, and determine the actual relative speed uniquely, when calculating the actual relative speed without folding by combining the relative speeds detected by the frequency modulation signal of each kind among plural kinds. A radar apparatus (1) sets a speed width (ΔVr) of the speed calculation range to less than a speed width that the actual relative speed can be calculated uniquely; and calculates an actual relative speed (Vr) which does not have folding due to a detection range of relative speed, within the speed calculation range.

The described aspects and implementations enable efficient calibration of a sensing system of an autonomous vehicle (AV). In one implementation, disclosed is a method and a system to perform the method, the system including the sensing system configured to collect sensing data and a data processing system, operatively coupled to the sensing system. The data processing system is configured to identify reference point(s) in an environment of the AV, determine multiple estimated locations of the reference point(s), and adjust parameters of the sensing system based on a loss function representative of differences of the estimated locations.

The technology relates to determining general weather conditions affecting the roadway around a vehicle, and how such conditions may impact driving and route planning for the vehicle when operating in an autonomous mode. For instance, the on-board sensor system may detect whether the road is generally icy as opposed to a small ice patch on a specific portion of the road surface. The system may also evaluate specific driving actions taken by the vehicle and/or other nearby vehicles. Based on such information, the vehicle's control system is able to use the resultant information to select an appropriate braking level or braking strategy. As a result, the system can detect and respond to different levels of adverse weather conditions. The on-board computer system may share road condition information with nearby vehicles and with remote assistance, so that it may be employed with broader fleet planning operations.

A vehicle (AV) includes a radar sensor and a hardware logic component. The radar sensor receives a radar return from a driving environment of the vehicle and outputs radar data that is indicative of the return to the hardware logic component. The hardware logic component further receives data indicative of a velocity of the vehicle from a sensor mounted on the vehicle. The hardware logic component is configured to employ synthetic aperture radar (SAR) techniques to compute a three-dimensional position of a point on a surface of an object in the driving environment of the vehicle based upon the radar data and the velocity of the vehicle.

In various examples, a hazard detection system fuses outputs from multiple sensors over time to determine a probability that a stationary object or hazard exists at a location. The system may then use sensor data to calculate a detection bounding shape for detected objects and, using the bounding shape, may generate a set of particles, each including a confidence value that an object exists at a corresponding location. The system may then capture additional sensor data by one or more sensors of the ego-machine that are different from those used to capture the first sensor data. To improve the accuracy of the confidences of the particles, the system may determine a correspondence between the first sensor data and the additional sensor data (e.g., depth sensor data), which may be used to filter out a portion of the particles and improve the depth predictions corresponding to the object.

A navigation system includes: a control circuit configured to: generate a video clip by parsing an interval of a sensor data stream for a region of travel; analyze the video clip submitted to a deep learning model, already trained, including identifying a traffic flow estimate; access a position coordinate for calculating a distance to intersection; generate a traffic flow state by fusing a corrected speed, the traffic flow estimate, and the distance to intersection; merge a vehicle maneuvering instruction into the traffic flow state for maneuvering through the region of travel; and a communication circuit, coupled to the control circuit, configured to: communicate the traffic flow state for displaying on a device.

A method and apparatus with vehicle radar control is disclosed. An apparatus with vehicle radar control includes a radio frequency (RF) transceiver including a transmitting antenna array and a receiving antenna array, and at least one processor configured to collect environmental information of the vehicle, determine a radar mode of the vehicle based on the collected environmental information, generate one or more control signal configured to control one or more of the transmitting antenna array and the receiving antenna array based on the determined radar mode, and provide the generated one or more control signals to the RF transceiver, wherein one or more of the transmitting antenna array and the receiving antenna array operate according to the one or more generated control signals.