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: 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: 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: 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: 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.

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: A brake system for a transportation vehicle, a transportation vehicle having a brake system, and a method for operating a brake system. The brake system has two control units, wherein the respective control unit actuates a respective brake circuit of the brake system, which includes two of four service brakes and one of two electric parking brakes of the brake system. In response to a defect in one of the brake circuits, the control unit of the other brake circuit actuates the respective brakes of the other brake circuit, to carry out trailer combination stabilization of a trailer combination having the transportation vehicle and a trailer coupled to the transportation vehicle; and/or to steer the transportation vehicle in the case of a defect in a steering system of the transportation vehicle based on a steering command of a control device for autonomous driving.

Abstract: Provided is a survey system capable of more highly accurately obtaining a product of a three-dimensional survey. A survey system includes a mobile body, a scanner including an emitting unit, a light receiving unit, a distance measuring unit, a first optical axis deflecting unit disposed on an optical axis of the distance measuring light and configured to deflect a distance measuring light, a second optical axis deflecting unit disposed on a light receiving optical axis of the reflected distance measuring light and configured to deflect a reflected distance measuring light at the same angle in the same direction as those of the first optical axis deflecting unit, and an emitting direction detecting unit to detect deflection angles and directions of the first and the second optical axis deflecting units, a posture detecting device of the scanner, and a position measuring device of the scanner.

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.

Abstract: An automated driving control unit sets an automated driving state to a first automated driving state (step S3), in a case that an automated driving instruction unit has instructed the initiation of automated driving in a state in which a destination is set by a destination setting unit, sets the automated driving state to a second automated driving state (step S4), in a case that the automated driving instruction unit has instructed the initiation of automated driving in a state in which the destination is not set by the destination setting unit, and causes the automated driving state to transition from the first automated driving state to the second automated driving state (step S13), in a case that a current travel position lies outside of a travel route during the travel control in the first automated driving state.

Abstract: Various examples are directed to systems and methods for navigating an autonomous vehicle. Trip plan data may describe a plurality of candidate vehicle start points, a plurality of candidate waypoints, and a plurality of candidate vehicle end points. A plurality of candidate routes may be determined between an algorithm start point and an algorithm end point. Each candidate route of the plurality of candidate routes may include at least one of the plurality of candidate waypoints and at least one of the plurality of candidate vehicle end points. A best rate may be determined using the plurality of candidate routes. The best route may include a first candidate vehicle start point, a first candidate waypoint, and a first candidate vehicle end point. The autonomous vehicle may be controlled along the best route from the first candidate vehicle start point towards the first candidate vehicle end point.

Abstract: A georeferenced trajectory system for vehicles receives trajectory data generated by a plurality of vehicle sensors and scaffolds of previously generated maps and aligns geometry data for a geographic region and trajectory data from the received data from different map builds. A scaffold of a geographic region to be mapped during an initial map build is generated, and the trajectory data from respective map builds is aligned with the scaffold of previously generated maps to generate a map of the geographic region. The resulting map expands the coverage of the existing map such that old and new map data is in a common consistent reference frame whereby the map may be built incrementally by merging or expanding local scaffolds and filling in the merged or expanded scaffold while ensuring global consistency.

Abstract: A laser range finder projects light while changing a direction in a horizontal direction at a predetermined angle with respect to a vertical direction to also receive reflected light of the light, and detects a direction and a distance in which the light is reflected from an obstacle or the like, according to a difference time between a time of light projection and a time of light reception. A normal direction of a flat plane forming a road surface is detected on the basis of a polarized image. The laser range finder projects light such that the light has a predetermined angle with respect to the vertical direction so as to be orthogonal to the normal direction of the flat plane forming the road surface. The laser range finder can be applied to in-vehicle systems.