Abstract: Apparatus, device, methods and system relating to a vehicular telemetry environment for identifying in real time unpredictable network communication faults in network zones based upon pre-processed raw telematics big data logs that may include GPS data and an indication of vehicle status data, and supplemental data that may further include location data and network data.

Abstract: An information generating device according to an embodiment includes one or more hardware processors. The one or more hardware processors: acquire environmental information indicating a state of environment around a vehicle; acquire abnormality information indicating an abnormality on a road, the abnormality having possibility of affecting traveling of the vehicle; acquire vehicle information including at least one of information on a traveling operation of the vehicle and information on specifications of the vehicle; and generate notification information indicating an operation to be performed by the vehicle against the abnormality on the road, the notification information being generated based on the environmental information, the abnormality information, and the vehicle information.

Abstract: Systems and methods are directed to matching an available vehicle to a rider requesting a service. In one example, a computer-implemented method includes obtaining, by a computing system comprising one or more computing devices, a service request from a rider. The method further includes obtaining, by the computing system, data indicative of a current location of the rider; and determining that the current location of the rider is within proximity of an autonomous vehicle queuing location. The method further includes providing, by the computing system, data to the rider to provide for selection of an available autonomous vehicle at the autonomous vehicle queuing location. The method further includes obtaining, by the computing system, rider authentication data upon a selection of an autonomous vehicle by the rider; and, in response to obtaining rider authentication data, matching an autonomous vehicle selected by the rider to provide for performance of the service request.

Abstract: An electronic controller (10) for a motor vehicle (100), the controller being configured to determine when at least one wheel (111, 112, 114, 115) has lost traction, wherein when the controller (10) determines that at least one wheel (111, 112, 114, 115) has lost traction the controller (10) is configured to provide an output to a driver indicative of the at least one wheel (111, 112, 114, 115) that has lost traction.

Abstract: Systems and methods are provided for forecasting the motion of actors within a surrounding environment of an autonomous platform. For example, a computing system of an autonomous platform can use machine-learned model(s) to generate actor-specific graphs with past motions of actors and the local map topology. The computing system can project the actor-specific graphs of all actors to a global graph. The global graph can allow the computing system to determine which actors may interact with one another by propagating information over the global graph. The computing system can distribute the interactions determined using the global graph to the individual actor-specific graphs. The computing system can then predict a motion trajectory for an actor based on the associated actor-specific graph, which captures the actor-to-actor interactions and actor-to-map relations.

Abstract: One or more devices in a data analysis computing system may be configured to receive and analyze acceleration data corresponding to driving data, analyze the acceleration data, and determine driving patterns and associated drivers based on the data. Acceleration data may be collected by one or more mobile devices, such as smartphones, tablet computers, and/or on-board vehicle systems. Drivers associated with driving trips may be identified based on the acceleration data collected by the mobile devices. In some cases, driving patterns may be determined based on the acceleration data before and after stopping points during driving trips, and the driving patterns may be compared to a set of previously stored driving patterns associated with various different drivers.

Abstract: A method is provided that includes: receiving a plurality of estimated position points, each estimated position point including a timestamp, where each estimated position point is an estimate of a position of a vehicle at a time respective timestamp; receiving on or more path events, where each of the one or more path events includes a timestamp and data from at least one sensor of the vehicle; generating a path from the plurality of estimated position points, where the estimated position points are arranged in order of ascending time represented by the respective timestamp; and interpolating between two of the estimated position points to determine a location corresponding to one of the one or more path events, where the timestamp of the one of the one or more path event corresponds to a time that is between the times represented by the timestamps of the two estimated position points.

Abstract: Devices, systems, and methods for determining aircraft orientation are described herein. One device includes instructions executable to determine a subsection of a rendering of a portion of an airport, wherein the subsection is associated with a particular stand of the airport, and wherein the subsection includes a plurality of ground travel pathways, determine a first subset of the plurality of pathways, a second subset of the plurality of pathways, and a third subset of the plurality of pathways, track a location of an outbound aircraft within the subsection of the rendering, determine an orientation of the aircraft according to a first set of rules responsive to a determination that the location of the aircraft is within the first or second subset of the plurality of pathways, and determine the orientation of the aircraft according to a second set of rules responsive to a determination that the location of the aircraft is within the third subset of the plurality of pathways.

Abstract: A method of generating map data indicating a deviation from expected jam conditions on a segment of a plurality of segments in an area covered by an electronic map, the segment having associated therewith a jam probability indicating the likelihood of a jam on that segment. The method comprises establishing an expected jam condition for the segment based on the jam probability of that segment, and then obtaining live data indicating the jam conditions on at least one of the plurality of other segments in the area. A revised jam condition can then be established for the segment using the obtained live data.

Abstract: This application is directed to on-vehicle behavior modeling of vehicles. A vehicle has one or more processors, memory, a plurality of sensors, and a vehicle control system. The vehicle collects training data via the plurality of sensors, and the training data include data for one or more vehicles during a collection period. The vehicle locally applies machine learning to train a vehicle driving behavior model using the collected training data. The vehicle driving behavior model is configured to predict a behavior of one or more vehicles. The vehicle subsequently collecting sensor data from the plurality of sensors and drives the vehicle by applying the vehicle driving behavior model to predict vehicle behavior based on the collected sensor data. The vehicle driving behavior model is configured to predict behavior of an ego vehicle and/or a distinct vehicle that appears near the ego vehicle.

Abstract: Embodiments disclose systems and methods to generate a lane-based reference line for an autonomous driving vehicle. In one embodiment, a system receives a request to route an autonomous driving vehicle (ADV) from a source location to a target location. In response to the request, the system identifies a set of road segments between the source location and the target location navigable by the ADV, where each of the road segments in the set includes one or more lane segments. The system identifies one or more lane paths corresponding to the set of road segments. The system determines an effective distance for each of the one or more lane paths. The system selects a lane path based on the determined effective distance. The system generates a lane-based reference line based on the selected lane path.

Abstract: A path providing device for providing a route to a vehicle includes a first communication module configured to receive a high-definition (HD) map information from an external server, a second communication module configured to receive external information generated by an external device located within a predetermined range from the vehicle, and a processor configured to generate forward path information for guiding the vehicle based on the HD map and provide the forward path information to at least one of electric components provided in the vehicle. The processor is configured to generate dynamic information related to an object to be sensed by the at least one of the electric components based on the external information and to match the dynamic information to the forward path information.

Abstract: An improved black box data recorder for use with autonomous driving vehicles (AVD). In one embodiment, two cyclic buffers are provided to record vehicle sensors data. A first cyclic buffer records raw vehicle sensor data on a volatile memory, while a second cyclic buffer records the same vehicle sensor data, as compressed data, on a non-volatile memory. In a case of a collision or near collision, in one embodiment the buffers are flushed into a non-volatile (NV) storage for retrieval. As long as there is no power interruption, the raw vehicle sensor data will be accessible from the NV storage. If a power interruption occurs, the raw vehicle sensor data held in the volatile memory of the first cyclic buffer will be lost and only the compressed form of the vehicle sensor data from the second cyclic buffer will survive and be accessible.

Abstract: The present disclosure relates to adaptive cruise control in the field of automatic driving, and discloses a vehicle control method, an apparatus, an electronic device and a storage medium. A specific implementation is: firstly, determining a target travelling scenario according to real-time monitoring data upon fulfilment of a preset update condition; then, determining a target time headway according to the target travelling scenario, where the target time headway is used to dynamically adjust a relative motion state between an host vehicle and a surrounding vehicle; and finally, controlling a vehicle according to the target time headway. It solves the problem of the prior art in overemphasizing the state of the vehicle ahead for automatic driving control while overlooking the perception of the driver or passenger of the host vehicle in the travelling scenario can prompt the driver to manually intervene, compromising the experience of the automatic driving.

Abstract: A control device of a vehicle is configured to control travelling of the vehicle such that the vehicle follows a preceding vehicle based on information received from the preceding vehicle, and determine, based on information regarding a cargo space of the preceding vehicle, an inter-vehicle distance to the preceding vehicle when the vehicles stop. Upon the preceding vehicle having stopped, the device stops the vehicle such that a distance between the vehicle and the preceding vehicle becomes the inter-vehicle distance determined by the device.

Abstract: In one embodiment, a system includes one or more processors and one or more computer-readable non-transitory storage media coupled to one or more of the processors. The one or more computer-readable non-transitory storage media include instructions operable when executed by one or more of the processors to cause the system to perform operations including receiving service-facility data associated with a service facility that includes one or more service regions for servicing autonomous vehicles. Each of the one or more service regions is configured to charge an autonomous vehicle. The service facility data includes location information indicating a location of each of the one or more service regions; and availability information indicating an availability of each of the one or more service regions. The operations also include receiving vehicle data associated with a number of autonomous vehicles. The vehicle data includes a charge level of a respective autonomous vehicle.

Abstract: Techniques and methods for determining a distance between a point within an environment and a reference line are discussed herein. For instance, a vehicle may be navigating. While navigating, the vehicle may receive a reference line that represents a road segment and determine various regions relative to the reference line. Additionally, the vehicle may generate sensor data representing the environment and identify an object using the sensor data. The vehicle may then determine that a location of the object corresponds to a region from the regions. Based on the region, the vehicle may determine a rule for identifying the distance between the vehicle and the reference line. The vehicle may then determine the distance using the rule, the location of the object, and the reference line. Additionally, the vehicle may determine an action for the vehicle to perform that is based on the distance.

Abstract: A hands-on determination device, a system including the same, and a vehicle control method thereof are provided. The hands-on determination device includes a processor that determines a hands-on state of a driver through motion recognition and determines whether to use a result of determination of the hands-on state based on at least one of a weather condition, a condition for validity of motion sensing, classification of a driving road, a vehicle control state, or a driver state and a storage storing data for motion recognition.

Abstract: Aspects of the disclosure relate to identifying and adjusting speed plans for controlling a vehicle in an autonomous driving mode. In one example, an initial speed plan for controlling speed of the vehicle for a first predetermined period of time corresponding to an amount of time along a route of the vehicle is identified. Data identifying an object and characteristics of that object is received from a perception system of the vehicle. A trajectory for the object that will intersect with the route at an intersection point at particular point in time is predicted using the data. A set of constraints is generated based on at least the trajectory. The speed plan is adjusted in order to satisfy the set of constraints for a second predetermined period of time corresponding to the amount of time. The vehicle is maneuvered in the autonomous driving mode according to the adjusted speed plan.

Abstract: Enclosed are embodiments for navigation with drivable area detection. In an embodiment, a method comprises: receiving a point cloud from a depth sensor, receiving image data from a camera; predicting at least one label indicating a drivable area by applying machine learning to the image data; labeling the point cloud using the at least one label; obtaining odometry information; generating a drivable area by registering the labeled point cloud and odometry information to a reference coordinate system; and controlling the vehicle to drive within the drivable area.