Abstract: Techniques to generate driving scenarios for autonomous vehicles characterize a path in a driving scenario according to metrics such as narrowness and effort. Nodes of the path are assigned a time for action to avoid collision from the node. The generated scenarios may be simulated in a computer.

Abstract: The invention relates to a method for preparing or generating a training data set for machine learning to operate a vehicle component. Provided multidimensional data points are divided up in a first step by dividing up the plurality of data points into multidimensional clusters by using a cluster algorithm. Then a training data set is generated by selecting data points from the basic training data set. The selection comprises determining a smallest cluster among the plurality of clusters with the lowest number of data points. Furthermore, at least one subset of the data points of the smallest cluster is provided for the training data set. In another step, a subset of data points is selected from each of the other clusters for the training data set, wherein the number of selected data points of each other cluster corresponds to the number of selected data points of the smallest cluster.

Abstract: Systems and methods for detecting a surprise or unexpected movement of an actor with respect to an autonomous vehicle are provided. An example computer-implemented method can include, for a first compute cycle, obtaining motion forecast data based on first sensor data collected with respect to an actor relative to an autonomous vehicle; and determining, based on the motion forecast data, failsafe region data representing an unexpected path or area where a likelihood of the actor following the unexpected path or entering the unexpected area is below a threshold. For a second compute cycle after the first compute cycle, the method can include obtaining second sensor data; determining, based on the second sensor data and the failsafe region data, that the actor has followed the unexpected path or entered the unexpected area; and in response to such determination, determining a deviation for controlling a movement of the autonomous vehicle.

Abstract: An industrial vehicle remote operation system includes an industrial vehicle including a vehicle communication part that performs wireless communication; a remote operation device including a remote communication part that performs wireless communication with the vehicle communication part, wherein the remote operation device is used to remotely operate the industrial vehicle; and a remote-operation stop control part that, based on a fact that a remote-operation stop operation has been performed on the remote operation device in a situation where operation of the industrial vehicle is being performed through a remote operation by the remote operation device, performs an operation stop control for stopping the operation of the industrial vehicle, and stops the remote operation by the remote operation device after the operation stop control is completed.

Abstract: A context-aware voice guidance method is provided that interacts with other voice services of a user device. The voice guidance does not provide audible guidance while the user is making a verbal request to any of the voice-activated services. Instead, the voice guidance transcribes its output on the screen while the verbal requests from the user are received. In some embodiments, the voice guidance only provides a short warning sound to get the user's attention while the user is speaking on a phone call or another voice-activated service is providing audible response to the user's inquires. The voice guidance in some embodiments distinguishes between music that can be ducked and spoken words, for example from an audiobook, that the user wants to pause instead of being skipped. The voice guidance ducks music but pauses spoken words of an audio book in order to provide voice guidance to the user.

Abstract: A light detection and ranging (LIDAR) sensor system mounted to a vehicle includes a first device and a second device coupled to the first device. The first device includes a laser source and one or more optical components. The first device is configured to output an optical signal associated with a local oscillator (LO) signal. The second device includes an optical amplifier array device and a transceiver device. The optical amplifier array device includes an integrated optical component and is configured to amplify the optical signal. The transceiver device is configured to transmit the amplified optical signal to an environment and receive a returned optical signal that is reflected from an object in the environment.

Abstract: In various examples, sensor data may be received that represents a field of view of a sensor of a vehicle located in a physical environment. The sensor data may be applied to a machine learning model that computes both a set of boundary points that correspond to a boundary dividing drivable free-space from non-drivable space in the physical environment and class labels for boundary points of the set of boundary points that correspond to the boundary. Locations within the physical environment may be determined from the set of boundary points represented by the sensor data, and the vehicle may be controlled through the physical environment within the drivable free-space using the locations and the class labels.

Abstract: The present disclosure relates to a positioning method, a positioning server, and a positioning system. The positioning method includes: receiving a wireless positioning coordinate of a moving object; determining a visual sub-map to be matched in a visual map database based on the wireless positioning coordinate; and obtaining a visual positioning coordinates corresponding to a current image of the moving object captured at time of wireless positioning as a positioning result, based on the determined visual sub-map to be matched.

Abstract: A vehicle weight sensing system particularly useful for trailers. An axle tube is mounted to the vehicle or trailer through its suspension members that may be leaf springs. A mounting block is affixed to the axle tube for mounting a strain gauge. The mounting block is fixed to the axle tube between the suspension members connected to the axle tube. The mounting block has a mounting surface opposite to the mating surface and a notch extends from the mating surface toward the mounting surface. The notch terminates between the mating surface and the mounting surface. The notch separates rigidified sections of the mounting block and the rigidified sections straddle the notch. The stain gauge measures strain in the axle and thereby generates a signal proportional to the weight on the trailer. The signal can be used to properly proportion a brake system on the trailer.

Abstract: A travel information processing apparatus includes a voice input device configured to input voice data of a user, an output device configured to specify an object by estimating from the voice data based on a word extracted from the voice data and indicating the object around a traveling route and a word extracted from the voice data and indicating a positional relationship of the object and output image data or voice data indicating a specified object; and a travel information processor configured to change a traveling motion of a subject vehicle based on the specified object.

Abstract: A computing system that analyzes the network effects of avoidance areas on autonomous vehicle routing is described herein. The computing system includes a data store that comprises a set of avoidance areas through which the autonomous vehicle is prohibited from being routed. A grouping system identifies groups of avoidance areas. A graph construction system constructs a graph representation of the avoidance area groups. A ranking algorithm is evaluated over the graph representation to generate a ranking of the avoidance area groups by relative impact on routing metrics for routes through an operational area of the autonomous vehicle. A mapping vehicle can be dispatched to resolve avoidance areas in avoidance area groups indicating in the ranking as having a greater impact on routing metrics than other avoidance area groups.

Abstract: The present disclosure provides a method comprising identifying at least one of a characteristic and an identity of an item for delivery from an origin to a destination and selecting one of a plurality of possible routes between the origin and the destination. For each of a plurality of route segments of the selected route, mapping information is used to characterize the route segment and one of a plurality of driving modes is selected for the route segment based on the characterization of the route segment and the at least one of the item characteristic and the item identity. A driving plan comprising a collection of the selected driving modes corresponding to the plurality of route segments comprising the selected route is provided to a vehicle and the vehicle delivers the item from the origin to the destination via the selected route using the driving plan.

Abstract: The present disclosure provides a method comprising identifying at least one of a characteristic and an identity of an item for delivery from an origin to a destination and selecting one of a plurality of possible routes between the origin and the destination. For each of a plurality of route segments of the selected route, mapping information is used to characterize the route segment and one of a plurality of driving modes is selected for the route segment based on the characterization of the route segment and the at least one of the item characteristic and the item identity. A driving plan comprising a collection of the selected driving modes corresponding to the plurality of route segments comprising the selected route is provided to a vehicle and the vehicle delivers the item from the origin to the destination via the selected route using the driving plan.

Abstract: A computer-implemented simulation method is described for testing a driving program for the at least partial autonomous guidance of a test vehicle. The test vehicle is moved by the driving program on a simulated road. A second vehicle in the simulation method is moved as a function of control commands of a user. The test vehicle and the second vehicle are displayed to the user including the road, data relating to the guidance of the test vehicle being collected and stored during the simulation method. A system is also described.

Abstract: A flight control method includes displaying a user interface configured to receive an operation instruction including coordinates of waypoints, generating route data of a route based on the coordinates of the waypoints, sending the route data to an aircraft to instruct the aircraft to execute the route, recording an interruption point during execution of the route by the aircraft, displaying a plurality of candidate starting points including at least one of the interruption point, a last waypoint of the route before the interruption point, a next waypoint of the route after the interruption point, or a user-designated waypoint, in response to a selection instruction, selecting a target starting point from the plurality of candidate starting points, and controlling the aircraft in an interrupted state to fly to the target starting point and resume the execution of the route from the target starting point.

Abstract: The present application discloses an automatic driving control method. In the method, parameters are optimally set by using a noisy and noiseless dual-strategy network, identical vehicle traffic environment state information is input into the noisy and noiseless dual-strategy network, a motion space perturbation threshold is set by using a noiseless strategy network as a comparison and a benchmark so as to adaptively adjust noise parameters, and motion noise is indirectly added by adaptively injecting noise into a strategy network parameter space, such that exploration of an environment and a motion space by a deep reinforcement learning algorithm may be effectively improved, automatic driving exploration performance and stability based on deep reinforcement learning is improved, and full consideration of influence of an environment state and driving strategies in vehicle decision-making and motion selection is ensured, thereby improving the stability and safety of an automatic vehicle.

Abstract: A damping control system for a vehicle having a suspension located between a plurality of ground engaging members and a vehicle frame includes at least one adjustable shock absorber having an adjustable damping profile.

Abstract: An agricultural machine includes a vehicle body including a front wheel and a rear wheel, a hood at a front portion of the vehicle body, a protector provided to the vehicle body to protect an operator seat, and an obstacle detector to detect an obstacle, the obstacle detector being located in front of the protector outwardly in a width direction of the hood.

Abstract: Disclosed in some examples, are devices, methods, systems, and machine readable mediums that provide for automated goods exchange between autonomous vehicles while the autonomous vehicles are still in motion. This may be used to efficiently ship packages long distances as well as to transfer goods to consumers. This allows shippers to transfer goods from one truck to another without having to stop and unload the truck, decreasing costs by limiting human involvement and improving efficiency. Likewise, mobile merchants, such as food trucks, may sell to consumers in cars without having to stop to perform the exchange, increasing the convenience to consumers.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices associated with trajectory prediction are provided. For example, trajectory data and goal path data can be accessed. The trajectory data can be associated with an object's predicted trajectory. The predicted trajectory can include waypoints associated with waypoint position uncertainty distributions that can be based on an expectation maximization technique. The goal path data can be associated with a goal path and include locations the object is predicted to travel. Solution waypoints for the object can be determined based on application of optimization techniques to the waypoints and waypoint position uncertainty distributions. The optimization techniques can include operations to maximize the probability of each of the solution waypoints. Stitched trajectory data can be generated based on the solution waypoints. The stitched trajectory data can be associated with portions of the solution waypoints and the goal path.