Abstract: A modular flat-packable drone kit includes a plurality of components that can be assembled into a drone. Components of the drone kit include elements that may be cut from a flat sheet of material, thereby enabling low cost manufacturing and compact packaging and may be assembled without specialized tools. A set of drones may operate in a standalone mode or may be coupled together and operated in a group configuration.

Abstract: Systems and techniques are provided for charging devices at a property using battery-charging drones. In some implementations, a monitoring system is configured to monitor a property and includes a battery-powered sensor configured to generate sensor data. The system includes a drone that is configured to navigate the property and charge the battery-powered sensor. A monitor control unit is configured to obtain a battery level from the battery-powered sensor and compare the battery level to a battery level threshold. Based on the comparison, the monitor control unit determines that the battery level does not satisfy the threshold. Based on the determination, the monitor control unit generates and transmits an instruction to a drone for the drone to navigate to the battery-powered sensor and charge a battery of the battery-powered sensor. The monitor control unit receives data from the drone that indicates whether the drone charged the battery of the sensor.

Abstract: Systems and methods are provided for vehicle navigation. In one implementation, at least one processor may be programmed to receive, from a camera, a captured image representative of features in an environment of the vehicle. The processor may generate a warped image based on the received captured image, which may simulate a view of the features in the environment of the vehicle from a simulated viewpoint elevated relative to an actual position of the camera. The processor may further identify a road feature represented in the warped image, which may be transformed in one or more respects relative to a representation of the road feature in the captured image. The processor may then determine a navigational action for the vehicle based on the identified feature represented in the warped image and cause at least one actuator system of the vehicle to implement the determined navigational action.

Abstract: A light detection and ranging (LIDAR) controller is disclosed. The LIDAR controller may determine, based on a position of an implement, a scan area of the LIDAR sensor, wherein the scan area has an increased point density relative to another area of a field of view, of the LIDAR sensor, that includes the implement. The LIDAR controller may cause the LIDAR sensor to capture, with the increased point density, LIDAR data associated with the scan area. The LIDAR controller may process the LIDAR data to determine whether an object of interest is in an environment of the machine that is associated with the scan area. The LIDAR controller may perform an action based on the environment of the machine.

Abstract: An autonomous vehicle having a lidar sensor system is described. A computing system is configured to determine that the lidar sensor system is to update a code that is included in light signals emitted by the lidar sensor system. The computing system transmits a command signal to the lidar sensor system, wherein the command signal causes the lidar sensor system to transition from emitting light signals with a first code therein to emitting light signals with a second code therein, wherein the first code is different from the second code.

Abstract: An apparatus for controlling an MDPS system including: an autonomous driving cancellation determination unit configured to determine whether to cancel autonomous driving, using column torque passed through a band stop filter, under an autonomous driving condition; and a signal processing unit configured to calculate command steering angle acceleration information using command steering angle information outputted from an autonomous driving system. When the steering angle acceleration information is equal to or greater than a predetermined reference value, the autonomous driving cancellation determination unit may determine that urgent steering is performed by the autonomous driving system, and forbid cancellation of the autonomous driving.

Abstract: System and methods for estimating a centerline of a road that separates traffic moving in opposite directions include aggregating a data set from each of a plurality of vehicles traversing the road over a period of time as telemetry data. Each data set of the telemetry data indicates a location and a heading. The method includes clustering the data sets of the telemetry data based on the heading indicated by each data set, and identifying a separator to separate the data sets indicating a first heading from the data sets indicating a second heading, opposite to the first heading. The centerline is estimated based on applying a spatial smoothing to the separator.

Abstract: A vehicle control device includes a failure detection unit configured to detect a failure of a vehicle, and a vehicle controller configured to control the vehicle. The vehicle controller changes the control of the vehicle depending on a failure level when the failure detection unit detects a failure of an on-vehicle component other than the vehicle control device.

Abstract: Ground truth data may be too sparse to supervise training of a machine-learned (ML) model enough to achieve an ML model with sufficient accuracy/recall. For example, in some cases, ground truth data may only be available for every third, tenth, or hundredth frame of raw data. Training an ML model to detect a velocity of an object when ground truth data for training is sparse may comprise training the ML model to predict a future position of the object based at least in part on image, radar, and/or lidar data (e.g., for which no ground truth may be available). The ML model may be altered based at least in part on a difference between ground truth data associated with a future time and the future position.

Abstract: Techniques for determining information associated with sounds detected in an environment based on audio data and map data or perception data are discussed herein. A vehicle can use map data and/or perception data to distinguish between multiple audio signals or sounds. A direct source of sound can be distinguished from a reflected source of sound by determining a direction of arrival of sounds and which objects the directions of arrival are associated with in the environment. A reflected sound can be received without receiving a direct sound. Based on the reflected sound and map data or perception data, characteristics of sound in an occluded region of the environment may be determined and used to control the vehicle.

Abstract: A steering apparatus includes an actuator and an actuator controller. The actuator is configured to generate an output for turning a steerable wheel of a vehicle. The actuator controller is configured to control the actuator in accordance with a steering input. The actuator controller includes a filtering processor configured to perform filtering processing for reducing or cutting a component in a predetermined frequency band from the steering input, and the actuator controller is configured to control the actuator in accordance with the steering input after the filtering processing.

Abstract: Included are: a timing determining unit for determining timing for preparing for transfer of driving authority to a driver on the basis of current position information of the vehicle, route information of the vehicle, and vehicle information of the vehicle; an awakening level calculating unit for calculating an awakening level of an occupant on the basis of occupant information; an authority transfer determining unit for determining whether the transfer of the driving authority is allowed on the basis of the calculated awakening level; and a vehicle state controlling unit for performing control to change a state of the vehicle before switching to the manual driving is announced to the occupant of the vehicle when it is determined that the transfer of the driving authority is not allowed.

Abstract: Some radar sensors may provide a Doppler measurement indicating a relative velocity of an object to a velocity of the radar sensor. Techniques for determining a two-or-more-dimensional velocity from one or more radar measurements associated with an object may comprise determining a data structure that comprises a yaw assumption and a set of weights to tune the influence of the yaw assumption. Determining the two-or-more-dimensional velocity may further comprise using the data structure as part of regression algorithm to determine a velocity and/or yaw rate associated with the object.

Abstract: Example implementations may relate to determining a strategy for a drop process associated with a light detection and ranging (LIDAR) device. In particular, the LIDAR device could emit light pulses and detect return light pulses, and could generate a set of data points representative of the detected return light pulses. The drop process could involve a computing system discarding data point(s) of the set and/or preventing emission of light pulse(s) by the LIDAR device. Accordingly, the computing system could detect a trigger to engage in the drop process, and may responsively (i) use information associated with the environment around the vehicle, operation of the vehicle, and/or operation of the LIDAR device as a basis to determine the strategy for the drop process, and (ii) engage in the drop process in accordance with the determined strategy.

Abstract: According to some examples of the presently disclosed subject matter there is provided a system and method for deploying a plurality of unmanned aerial vehicles (UAVs) by an airborne carrier aircraft for dispersing payload material, each UAV comprising at least one container containing payload material and being configured to disperse the payload material at a designated dispersion area in an event site.

Abstract: An autonomous driving controller includes: a processor to collect driving data when a vehicle is traveling and calculate a steering override reference value, which is a criterion of determining an override mode, based on the collected driving data; and a storage to store the collected driving data and a set of instructions executed by the processor to calculate the steering override reference value. In particular, the processor controls autonomous driving by varying the steering override reference value based on the collected driving data or information regarding a driver of the vehicle.

Abstract: Extrinsic calibration of a Light Detection and Ranging (LiDAR) sensor and a camera can comprise constructing a first plurality of reconstructed calibration targets in a three-dimensional space based on physical calibration targets detected from input from the LiDAR and a second plurality of reconstructed calibration targets in the three-dimensional space based on physical calibration targets detected from input from the camera. Reconstructed calibration targets in the first and second plurality of reconstructed calibration targets can be matched and a six-degree of freedom rigid body transformation of the LiDAR and camera can be computed based on the matched reconstructed calibration targets. A projection of the LiDAR to the camera can be computed based on the computed six-degree of freedom rigid body transformation.

Abstract: In one embodiment, a method includes accessing a set of data points captured using a radar system of the vehicle. Each data point is associated with at least three measurements include a Doppler measurement, a range measurement, and an azimuth measurement in reference to the radar system. The method also includes clustering the set of data points into one or more first clusters based on a first pair of the three measurements associated with each of the data points; and clustering the set of data points into one or more second clusters based on a second pair of the three measurements associated with each of the data points. The second pair being different from the first pair of the three measurements.

Abstract: Among other things, we describe techniques for detecting objects in the environment surrounding a vehicle. A computer system is configured to receive a set of measurements from a sensor of a vehicle. The set of measurements includes a plurality of data points that represent a plurality of objects in a 3D space surrounding the vehicle. The system divides the 3D space into a plurality of pillars. The system then assigns each data point of the plurality of data points to a pillar in the plurality of pillars. The system generates a pseudo-image based on the plurality of pillars. The pseudo-image includes, for each pillar of the plurality of pillars, a corresponding feature representation of data points assigned to the pillar. The system detects the plurality of objects based on an analysis of the pseudo-image. The system then operates the vehicle based upon the detecting of the objects.

Abstract: Systems and methods for controlling an autonomous vehicle to reduce idle data usage and vehicle downtime are provided. In one example embodiment, a computing system can obtain data associated with autonomous vehicle(s) that are online with a service entity. The computing system can obtain data indicative of the geographic area with an imbalance in a number of vehicles associated with the geographic area. The computing system can determine a first autonomous vehicle for re-positioning with respect to the geographic area based at least in part on the data associated with the one or more autonomous vehicles and the data indicative of the geographic. The computing system can communicating data indicative of a first re-positioning assignment associated with the first autonomous vehicle. In some implementations, the computing system can generate vehicle service incentive to entice a vehicle provider to re-position its autonomous vehicles with respect to the geographic area.