Abstract: Some embodiments of the present invention comprise a method of controlling a powertrain of a vehicle, the powertrain comprising drive torque means, a transmission and a driveline, the drive torque means comprising an internal combustion engine, the method comprising: detecting that coasting entry criteria have been met; causing the powertrain to assume a first coasting mode for a first time period in which the drive torque means delivers positive drive torque to the driveline to substantially balance powertrain losses; determining a value for at least one parameter, the or each parameter being indicative of: a probability that a demand will be made for torque to be delivered to the driveline in addition to torque delivered to substantially balance powertrain losses; or a probability that a demand will be made for braking of the vehicle; and setting the value of the first time period in dependence on the value of the or each parameter.

Abstract: Methods, apparatus, computer program products Geohash-based traffic management are provided. The method comprises receiving, by one or more processing units, respective operating status data of one or more vehicles travelling within a Geohash cell; generating, by one or more processing units, one or more service messages based on the received respective operating status data according to one or more service specifications; and providing, by one or more processing units, at least one of the one or more service messages responsive to a request from a vehicle travelling within the Geohash cell.

Abstract: An amusement park system in accordance with present embodiments includes multiple separated park areas, an autonomous vehicle configured to drive along ground surfaces within the multiple separated park areas, and a gondola system configured to transport the autonomous vehicle between the multiple separated park areas. The amusement park system further includes a control system configured to operate the autonomous vehicle to engage with and disengage from the gondola system to facilitate transport of the autonomous vehicle by the gondola system.

Abstract: A vehicle learning system includes a first execution device mounted on a vehicle, a second execution device outside the vehicle, and a storage device. The storage device stores mapping data including data, which is learned by machine learning and defines mapping that receives input data based on a detection value of an in-vehicle sensor and outputs an output value. The first execution device and the second execution device execute, in cooperation with each other, an acquisition process of acquiring input data, a calculation process of calculating an output value with the input data as an input of the mapping, and a relationship evaluation process of evaluating a relationship between a predetermined variable different from a variable corresponding to the output value and accuracy of the output value. The first execution device executes at least the acquisition process, and the second execution device executes at least the relationship evaluation process.

Abstract: The disclosed systems can regulate access to an online mode for a dynamic transportation matching system. For example, based on a provider efficiency parameter associated with the dynamic transportation matching system, the disclosed systems can prevent a transportation provider device from switching to the online mode within a geographic area. In addition, the disclosed systems can detect a pattern of behavior and, based on a comparison between the pattern of behavior and a behavioral threshold, cause a transportation provider device to switch from the online mode to an offline mode. Further, the disclosed systems can provide a map interface that indicates where a transportation provider device can switch from the offline mode to the online mode. Additionally, the disclosed systems can determine priorities associated with transportation provider devices and, based on the prioritization, selectively allow the transportation provider devices to switch from the offline mode to the online mode.

Abstract: Reckless behavior of drivers like, speeding, sudden acceleration and swerving through lanes can cause fatality and financial loss. Conventional methods mainly focus on driving style classification. The conventional methods mainly focus on driver classification and are not able to provide trip classification of a driver. Hence there is a challenge in trip classification of the driver based on acceleration data. The present disclosure for trip classification addresses the problem of end to end trip classification based on the acceleration data. Here, a journey is segmented into a plurality of sub-journey segments and each sub-journey segment is associated with a plurality of driving events. An event score is calculated for each sub-journey and a normalization is performed on the event score. Further, the journey is classified into at least one of good, average or bad based on the normalized data by utilizing a fuzzy based classification.

Abstract: A method for perception-sharing between similarly-situated vehicles that are traveling on a portion of a roadway that is equipped with an intelligent vehicle highway system is described, and includes executing, in a multi-access edge computing cluster in communication with a roadside unit disposed to monitor a roadway, an application-layer routine. The application-layer routine includes collecting real-time data associated with a plurality of objects from each of the similarly-situated vehicles, predicting motion of each of the plurality of objects based upon the real-time data, object-matching the motion of each of the plurality of objects, and executing fusion of the plurality of objects based upon the object-matching of the motion of each of the plurality of objects. Locations of the similarly-situated vehicles traveling on the roadway are identified based upon the fusion of the plurality of objects. The locations are communicated to one of the similarly-situated vehicles.

Abstract: A system and a method for the determination and forecast of absolute and relative risks for car accidents based on exclusively non-insurance related measuring data and based on automated traffic pattern recognition, wherein data records of accident events are generated and location-dependent probability values for specific accident conditions associated with the risk of car accident are determined. The proposed system provides a grid-based, technically new way of automation of risk-prediction related to motor accidents using environment based factors including socio-economic factors that are impacting motor traffic and are location dependent received from appropriate measuring devices and systems. In this way, predictions of the accident risk for arbitrary areas can be provided. The system is calibrated by comparing features of areas or road segments with the number and type of accidents that have measured or registered there, linking the features and accident data e.g.

Abstract: Sensor data is received from an array of sensors configured to capture one or more objects in an external environment of an autonomous vehicle. A first sensor group is selected from the array of sensors based on proximity data or environmental contexts. First sensor data from the first sensor group is prioritized for transmission based on the proximity data or environmental contexts.

Abstract: Methods and systems for traffic jam management include generating a representation of a road network that comprises a road graph, with vertices of the road graph representing road segments and edges of the road graph representing pairs of adjacent road segments at each intersection. The road graph is partitioned into sub-graphs to balance a number of messages received from vehicles within each sub-graph and to minimize vehicle transitions between sub-graphs. It is determined that a traffic jam is present on one or more road segments. Navigational information is provided to one or more vehicles responsive to the traffic jam.

Abstract: A method, system and apparatus for accessing an internal database that includes at least one payroll database, identifying an individual, a first location, a second location, a future date, and a time of day. Responsive to identifying the individual, the first location, the second location, the date, and the time of day, determining a predicted travel time for the individual between the first location and the second location at the time of day on the future date.

Abstract: A processor-implemented method includes storing, for each of multiple road sections, section-specific road-nonlinearity values relating to road-nonlinearity characteristics of the respective road section. The method includes receiving, from a user, user-desired route-specific road-nonlinearity values relating to the road-nonlinearity characteristics. The method further includes calculating, from the stored section-specific road-nonlinearity values, for a candidate route, one or one route-specific road-nonlinearity values relating to the road-nonlinearity characteristics. The candidate route's route-specific road-nonlinearity values are compared to the user-desired route-specific road-nonlinearity values. Based on results of the comparison, the candidate route is select to be a recommended route. The recommended route is communicated to the user.

Abstract: Methods and a system are described for prioritizing the upload of connected vehicle map data in a central mapping system. A central mapping unit includes a central server which receives and processes data uploads by priority order, from highest priority to lowest priority. The uploaded data is compared to central map data to determine whether the central map requires updating. When the central mapping unit determines that there is not enough information to update the central map in a region, a priority list is updated to give high priority to data collection in the region. The updated priority list is pushed to connected vehicles travelling in the region, which have external sensors for data collection in the surrounding environment. When a connected vehicle enters the region, its external sensors collect data regarding the region, which is uploaded to the central mapping unit with highest priority.

Abstract: Various technologies described herein pertain to autonomous vehicle consumption of real-time public transportation data to guide curb access and usage. An autonomous vehicle receives a trip request for a ride specifying a requested pullover location. The autonomous vehicle receives public transportation data specifying an expected arrival time of a public transportation vehicle at a reserved zone within proximity of the requested pullover location. The autonomous vehicle evaluates availability of the reserved zone during an expected occupancy time of the reserved zone by the autonomous vehicle based on the expected arrival time of the public transportation vehicle at the reserved zone. The autonomous vehicle selects an actual pullover location for the ride in the autonomous vehicle based on the availability of the reserved zone during the expected occupancy time. The autonomous vehicle stops at the actual pullover location for the ride in the autonomous vehicle.

Abstract: The embodiments herein provide a system and method for controlling an operation of the components in the two/three wheeled vehicle through a smartphone. The system comprises a vehicle module, a smartphone and a cloud computing system. The vehicle module reads the vehicle parameters with a vehicle parameter reader and sensors. The read vehicle parameter are forwarded to the microprocessor for transmission to the smartphone through a transceiver using a wired or wireless network. The microprocessor sends the vehicle parameters data to the smartphone for generating the commands/events for controlling an operation of the parts/components of the vehicle through the vehicle parameter controller. The data is uploaded to a cloud computing system and analyzed using an artificial intelligence module, a machine learning module and a pattern recognition module for providing insights/predictions to the user for controlling the vehicle.

Abstract: In one embodiment, a method includes the operation of segmenting a raw reference line into a plurality of reference line segments, including a first reference line segment, a second reference segment, and a third reference line segment in a sequential order, in response to receiving the raw reference line representing a route from a first location to a second location associated with an autonomous driving vehicle (ADV). The method further includes the operations of smoothing the first reference line segment and the third reference line segment using a Quadratic programming (QP) spline smoother; and smoothing the second reference line segment using a spiral smoother. Smoothed reference line segments from the plurality of reference line segments are connected to generate a smoothed reference line, which is to be used as a reference line of the route to control the ADV.

Abstract: Aspects of the present disclosure provide a computer-implemented method that includes, responsive to receiving a pickup location request indicating a pickup location, identifying a vehicle in a vicinity associated with the pickup location, the vehicle having vehicle location data associated therewith. The method further includes selecting a shortest route from the vehicle to the pickup location. The method further includes, responsive to determining that a crossing is present along the shortest route, receiving target object location data for a target object associated with the crossing. The method further includes determining whether the target object and the vehicle intersect are predicted to intersect at the crossing. The method further includes, responsive to determining the target object and the vehicle are predicted to intersect at the crossing, identifying another vehicle in the vicinity, wherein the other vehicle traverses a route that avoids the crossing.

Abstract: A computing device can present a map user interface for users to suggest updates to entry point data for a place, such as points of interest to the user. For an entry point, the computing device can present an entry point indicator that remains in a fixed position on a map view. The user may move the map view as the entry point indicator remains fixed. In response to inputs, the computing device can adjust the map view, so that the fixed entry point indicator lies in a new position relative to the point of interest. The computing device can also insert an entry point indicator on the map view. In response to user inputs, the computing device can associate a location of the inserted entry point indicator with the location of an entry point. The computing device can send the entry point data changes to a server for implementation.

Abstract: The vehicle electronic control apparatus includes two or more FPGAs that each receive information from the sensor, a nonvolatile memory that holds a bit stream for determining a configuration for the FPGA, an MCU that incorporates two or more nonvolatile memories, in each of which a program code for calculating the control command is installed, and an error control module that outputs an error signal to the outside at a time when an abnormality occurs in at least one of the FPGA and the nonvolatile memory incorporated in the MCU; the nonvolatile memory that holds the bit stream and the nonvolatile memory incorporated in the MCU can be accessed from the outside.

Abstract: The embodiments of the present disclosure provide a method and system for patrolling an expressway by unmanned aerial vehicles (UAVs). The method is applied to the system including a control center, a plurality of UAVs and a plurality of advertising board charging base stations. The method firstly receives unmanned aerial vehicle (UAV) status information sent by the plurality of unmanned aerial vehicles and road section patrolling data through the control center, and receives advertising board charging base station information sent by the plurality of advertising board charging base stations, then classifies, stores and analyzes the received data and information, and finally generates and sends control commands for the UAVs and charging advertising boards.