Abstract: An autonomous vehicle includes one or more sensors for detecting an object in an environment surrounding the autonomous vehicle and a vehicle computing system comprising one or more processors receiving canonical route data associated with at least one canonical route, and controlling travel of the autonomous vehicle based on sensor data from the one or more sensors and the canonical route data associated with the at least one canonical route. The at least one canonical route comprises at least one roadway connected with another roadway in a plurality of roadways in a geographic location that satisfies at least one route optimization function derived based on trip data associated with one or more traversals of the plurality of roadways in a geographic location by one or more autonomous vehicles.

Abstract: A vehicle driving system is equipped with an automatic driving control unit that outputs a signal for automatically actuating a plurality of pieces of in-vehicle operational equipment that can be operated by a driver, a manual driving control unit that outputs a signal to the pieces of the in-vehicle operational equipment based on an operational input by the driver, and an interface control unit that is connected to the automatic driving control unit, the manual driving control unit and the pieces of the in-vehicle operational equipment, that receives at least one of the signal output from the automatic driving control unit or the signal output from the manual driving control unit, and that outputs the received signal to the pieces of the in-vehicle operational equipment.

Abstract: A system and method for providing a comprehensive trajectory planner for a person-following vehicle that includes receiving image data and LiDAR data associated with a surrounding environment of a vehicle. The system and method also include analyzing the image data and detecting the person to be followed that is within an image and analyzing the LiDAR data and detecting an obstacle that is located within a predetermined distance from the vehicle. The system and method further include executing a trajectory planning algorithm based on fused data associated with the detected person and the detected obstacle.

Abstract: Real-time decision-making for a vehicle using belief state determination is described. Operational environment data is received while the vehicle is traversing a vehicle transportation network, where the data includes data associated with an external object. An operational environment monitor establishes an observation that relates the object to a distinct vehicle operation scenario. A belief state model of the monitor computes a belief state for the observation directly from the operational environment data. The monitor provides the computed belief state to a decision component implementing a policy that maps a respective belief state for the object within the distinct vehicle operation scenario to a respective candidate vehicle control action. A candidate vehicle control action is received from the policy of the decision component, and a vehicle control action is selected for traversing the vehicle transportation from any available candidate vehicle control actions.

Abstract: A method and a device for operating an automated vehicle. The method includes a step of detecting surroundings data values, a step of determining positions and/or predicted movements of objects in the surroundings of the automated vehicle, a step of carrying out a first comparison of the surroundings data values and/or of the positions and/or of the predicted movements using an external server, a step of determining a driving strategy for the automated vehicle as a function of the positions and/or predicted movements of the objects and as a function of the first comparison, a step of carrying out a second comparison of the driving strategy using the external server, and a step of operating the automated vehicle as a function of the driving strategy and as a function of the second comparison.

Abstract: Methods and systems for multi-hypothesis object tracking for automated driving systems. One system includes an electronic processor configured to receive environment information and generate pseudo-measurement data associated with an object within an environment of the vehicle. The electronic processor is also configured to determine, based on the environment information and the pseudo-measurement data, a set of association hypotheses regarding the object. The electronic processor is also configured to determine, based on the set of association hypotheses, an object state of the object. The electronic processor is also configured to control the vehicle based on the determined object state.

Abstract: Systems, methods, and vehicles for taking a vehicle out-of-service are provided. In one example embodiment, a method includes obtaining, by one or more computing devices on-board an autonomous vehicle, data indicative of one or more parameters associated with the autonomous vehicle. The autonomous vehicle is configured to provide a vehicle service to one or more users of the vehicle service. The method includes determining, by the computing devices, an existence of a fault associated with the autonomous vehicle based at least in part on the one or more parameters associated with the autonomous vehicle. The method includes determining, by the computing devices, one or more actions to be performed by the autonomous vehicle based at least in part on the existence of the fault. The method includes performing, by the computing devices, one or more of the actions to take the autonomous vehicle out-of-service based at least in part on the fault.

Abstract: Techniques for compensating for errors in position of a vehicle are discussed herein. In some cases, a discrepancy may exist between a measured state of the vehicle and a desired state as determined by a system of the vehicle. Techniques and methods for a planning architecture of an autonomous vehicle that is able to provide maintain a smooth trajectory as the vehicle follows a planned path or route. In some cases, a planning architecture of the autonomous vehicle may compensate for differences between an estimated state and a planned path without the use of a separate system. In this example process, the planning architecture may include a mission planning system, a decision system, and a tracking system that together output a trajectory for a drive system.

Abstract: A vehicle may have a seat and front wheel similar to those of a bicycle. However, the vehicle may have two rear wheels. The position of the two rear wheels may be adjustable. When the two rear wheels touch each other, the vehicle may function as a bicycle. When they are moved apart, the vehicle may function as a tricycle. In tricycle mode, the vehicle may travel autonomously without a human rider. In bicycle mode, a human may ride the vehicle like a bicycle. To transition between bicycle and tricycle modes, each rear wheel may rotate about a horizontal axis that is parallel to a longitudinal axis of the vehicle. In some cases, a bike chain is connected to only one of the rear wheels. In bicycle mode, motion imparted by the chain to one rear wheel may be transferred to the other by friction.

Abstract: A method is provided for steering control of a vehicle by using lateral velocity of two know points (or lateral velocity of one known point and yaw rate), longitudinal velocity and steer angle information. These factors are used to calculate a target steer angle based on the track angle based on yaw decomposition to thus adjust a current steer angle of the vehicle based on the target steer angle.

Abstract: A method can include sending, via a processing device, a signal to at least two of a plurality of location indicators from an autonomous vehicle in motion and transporting equipment or passengers. The method can further include receiving signals from the at least two location indicators. The method can further include determining a location of the autonomous vehicle within an indoor facility based on the received signals. The method can further include comparing the determined location to a corresponding pre-determined location. The method can further include, in response to the determined location being different than the pre-determined location, adjusting a direction of the autonomous vehicle along a predetermined path within the indoor facility.

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.