Abstract: A vehicle diagnostic system includes a first diagnostic server including a diagnostic database having historical data matched with possible vehicle fixes, and configured to receive retrieved vehicle data and identify a most likely vehicle fix associated therewith. The first diagnostic server is associated with a first processing capability. The system additionally includes a second diagnostic server including a diagnostic algorithm operatively associated therewith and configured to identify a possible vehicle fix based on an assessment of the retrieved diagnostic data according to predefined criteria associated with the diagnostic algorithm. The second diagnostic server is associated with a second processing capability.

Abstract: Embodiments of the present disclosure provide geolocation based operation of a vehicle by generating a configuration profile for one or more geolocations along a particular route. For each of a plurality of trips a vehicle takes along a route, a set of sensors may be polled at regular intervals as the vehicle travels along the route to obtain vehicle trip data at each interval. The vehicle trip data obtained at each interval may be associated with a geolocation of the vehicle at the interval. For each of the one or more geolocations of the vehicle along the route, a corresponding configuration profile may be generated based on the vehicle trip data associated with the geolocation across the plurality of trips to generate a set of configuration profiles, wherein each of the set of configuration profiles comprises instructions to modify a configuration setting of one or more components of the vehicle.

Abstract: The disclosure is an information processing device that manages an operation of an autonomous vehicle having a loading space in which a parcel is loaded. The information processing device includes a control unit that executes: acquiring operation plan information including a route along which the autonomous vehicle travels when the autonomous vehicle delivers the parcel, and a delivery time for the parcel; and acquiring vacant space information based on the operation plan information, the vacant space information including a traveling section where a vacant space is generated in the loading space of the autonomous vehicle, a time when the vacant space is generated in the loading space of the autonomous vehicle, and the size of the vacant space that is generated in the loading space of the autonomous vehicle.

Abstract: Provided are methods for generating future maneuver parameters in a planner, which include receiving at least one first set of parameters associated with one or more previous maneuvers of a vehicle and at least one second set of parameters associated with a maneuver goal of the vehicle, generating, using the first and second sets of parameters, a future maneuver parameter corresponding to a future maneuver of the vehicle, training at least one data model by comparing the generated future maneuver parameter to one or more reference maneuver parameters, generating, based on training, a corrected future maneuver parameter. The corrected future maneuver parameter includes a future maneuver of the vehicle and a correction to the future maneuver of the vehicle. The first set of parameters includes the generated corrected future maneuver parameter, which is used to correct at least one first parameter. The training is executed using the corrected first parameter.

Abstract: A method for generating a trajectory for a vehicle based on an abstract space of control parameters associated with the vehicle may include applying a machine learning model to determine an abstract space representation of the trajectory, which includes a sequence of control parameters associated with the vehicle. For instance, application of the machine learning model may include performing a search of the abstract space parameterized by control parameters that include derivatives of the position of the vehicle. Examples of control parameters include velocity, acceleration, jerk, and snap. A physical space representation of the trajectory may be determined by at least mapping the sequence of control parameters to a sequence of positions for the vehicle. A motion of the vehicle may be controlled based at least on the physical space representation of the trajectory. Related systems and computer program products are also provided.

Abstract: Systems and methods related to designation, design, and service of replaceable components of aerial vehicles may include designating a component as a replaceable component based on a determined service frequency, designing the replaceable component for incorporation into an aerial vehicle based on the service frequency, and servicing the replaceable component according to the service frequency. In this manner, a replaceable component having a relatively high service frequency may be incorporated into an aerial vehicle at a relatively more accessible location, and a replaceable component having a relatively low service frequency may be incorporated into an aerial vehicle at a relatively less accessible location, thereby facilitating efficient and reliable maintenance of replaceable components.

Abstract: A system and method for determining backlash in a driving system of a vehicle are provided. The system includes a motor speed sensor that detects a motor speed and a wheel speed sensor that detects a wheel speed. A speed difference determination unit receives the detected motor and wheel speeds and determines a rotational speed difference of the driving system. A torsion speed determination unit receives the determined rotational speed difference and determines a reference torsion speed for backlash determination of the driving system. A backlash determination unit receives the determined rotational speed difference and the determined reference torsion speed, determines a backlash speed corresponding to a difference between the rotational speed difference and the reference torsion speed, and determines whether backlash in the driving system occurs using the determined backlash speed.

Abstract: A traveling route generating device is provided, which may include a user interface and processing circuitry. The user interface may receive a home route that is a route on which a ship travels upon departing from a home harbor or upon returning to the home harbor, the home harbor being a location from which the ship departs. The processing circuitry may generate a traveling route between one of a destination of the ship and a current location of the ship, and a location on the home route.

Abstract: System, apparatus, device and methods relating to a telematic vehicle sharing platform ecosystem and a telematic vehicle share I/O expander to automate sharing and management of a vehicle that is shared by more than one operator.

Abstract: An impression estimation system for estimating visual impressions of a plurality of vehicles configured with exterior graphic messaging. A mobile device application of each of a plurality of mobile devices associated with the plurality of vehicles generates vehicle mobility data. A computational platform includes a processor and program code which, when executed by the processor, causes the processor to, with respect to each vehicle of the plurality of vehicles: (i) identify, using the vehicle mobility data, one or more of the road segments in which the vehicle was present; (ii) estimate, using the traffic volume data, a number of potential visual impressions of the vehicle within the road segments; (iii) determine an estimated number of visual impressions; (iv) generate visual impression information based at least in part upon the estimated number of visual impressions; and (v) cause display of the visual impression information.

Abstract: An electric power system for an electric vehicle, including: first charging and discharging current paths connected to a first battery unit; second charging and discharging current paths connected to a second battery unit; third charging and discharging current paths for a connection with an electric control system of the vehicle; a first power switch between the first discharging current path and the third discharging current path; a second power switch between the second discharging current path and the third discharging current path; a controlling unit to set the first and second power switches based on states of the accumulators, at least one of the first and second battery units being swappable. A controller, a method, and a vehicle comprising the system are also disclosed.

Abstract: Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling routing of an autonomous vehicles (AV) by identifying routes from a first location to a second location, identifying a target efficiency value of autonomous driving along a respective route, determining, in view of historical data for the respective route and using one or more randomized conditions, a confidence level associated with the target efficiency value, selecting, based on the target efficiency values and the associated confidence level, a preferred route, and causing the AV to select the first route for travel from the first location to the second location.

Abstract: A method includes obtaining sensor data captured by a sensor of an aircraft during a power up event. The sensor data includes multiple parameter values, each corresponding to a sample period. The method further includes determining a set of delta values, each indicating a difference between parameter values for consecutive sample periods of the sensor data. The method further includes determining a set of quantized delta values by assigning the delta values to quantization bins based on magnitudes of the delta values. The method further includes determining a normalized count of delta values for each quantization bin. The method further includes comparing the normalized counts of delta values to anomaly detection thresholds. The method further includes generating, based on the comparisons, output indicating whether the sensor data is indicative of an operational anomaly.

Abstract: According the present invention, there is provided a control system (10) that includes an image acquisition unit (11) that acquires an image generated by a camera, a controlled object determination unit (12) that analyzes the image and determines at least one of a vehicle that satisfies a predetermined condition and a vehicle in which a predetermined person is riding included in the image, as a vehicle to be controlled, a control content decision unit (13) that decides a control content for the vehicle to be controlled, and an output unit (14) that outputs a control command including the control content to the vehicle to be controlled.

Abstract: Example aspects of the present disclosure are directed to improved systems and methods for testing autonomous vehicle operation through the use of mobile test devices that are controlled at least in part in response to predictive motion planning associated with autonomous vehicles. More particularly, the motion of a mobile test device can be controlled based on the motion plan of an autonomous vehicle to cause desired interactions between the mobile test device and the autonomous vehicle. Motion planning data associated with the autonomous vehicle can be obtained by an autonomous vehicle (AV) test system prior to the autonomous vehicle implementing or completely implementing a motion plan. In this manner, the AV test system can proactively control the mobile test device based on predictive motion planning data to facilitate interactions between the mobile test device and the autonomous vehicle that may not otherwise be achievable.

Abstract: Aspects of the disclosure provide for reducing inconvenience to other road users caused by stopped autonomous vehicles. As an example, a vehicle having an autonomous driving mode may be stopped at a first location. While the vehicle is stopped, sensor data is received from a perception system of the vehicle. The sensor data may identify a road user. Using the sensor data, a value indicative of a level of inconvenience to the road user caused by stopping the vehicle at the first location may be determined. The vehicle is controlled in the autonomous driving mode to cause the vehicle to move from the first location and in order to reduce the value.

Abstract: This travel control device is provided with a controller for controlling travel of a vehicle fitted with tires, using automated driving. The vehicle travels on a course including a testing section for testing the tire. The controller controls travel of the vehicle so that the vehicle travels on the testing section based on a prescribed standard for testing of the tire.

Abstract: A system, a method and a computer program product are provided for evaluating quality of data, such as sensor data and map data, using a machine learning model. The system may include at least one memory configured to store computer executable instructions and at least one processor configured to execute the computer executable instructions to obtain first sensor features of the first sensor data associated with a road object in a first geographic region, first map features of the first map data associated with the road object and ground truth data associated with the road object. The processor may be configured to generate the machine learning model by configuring the ground truth data and calculating first information scores for each of the first sensor features and the first map features by recursively splitting each of the first sensor features and the first map features.

Abstract: An example method involves receiving, at a computing system, vehicle diagnostic information from a vehicle. The vehicle diagnostic information may include one or more sets of parameters corresponding to parameter identifiers (PIDs). The method further involves identifying a first set of parameters corresponding to a PID representing a state of a particular system of the vehicle and determining, using the first set of parameters, a current value of the PID. The method may also involve performing a comparison between the current value and a predetermined value for the PID and determining a health of the particular system of the vehicle such that the health reflects a difference between the current value and the predetermined value for the PID. The method may also involve displaying, by the computing system at a graphical interface, a vehicle health record representing the health of the particular system of the vehicle.

Abstract: Embodiments of the present invention proposes a method to automatically identify the flying targets by physical information (coordinates, heading, speed), time, and identification information (3/A code). This method includes two steps: features extraction and building machine learning model. In the features extraction step, features which are extracted include: cell indexes corresponding to coordinates, information of flight path, time in day/night format, heading, speed, and 3/A code, constructing n-dimensional vector. This vector is used as input for training a Random Forest model, to automatically identify class label of flying targets.