Abstract: A vehicle includes a first axle system operatively connected to a first set of wheels, a second axle system operatively connected to a second set of wheels, a first drive system operatively connected to the first set of wheels, a second drive system operatively connected to the second set of wheels independent of the first set of wheels, and a traction management control module electrically coupled to at least one of the first and second drive systems. The traction management control module calculates a torque capability of the corresponding one of the first and second axle systems and selectively transmits an axle torque command to the corresponding one of the first and second axle systems based on the torque capability.

Abstract: Systems, processes, and techniques for calibrating an onboard sensor of a vehicle are presented here. The vehicle has a control system that is capable of performing at least some of the tasks related to the calibration procedure. An exemplary methodology collects vehicle status data and obtains navigation map data during operation of the vehicle. A current calibration factor is calculated for the onboard sensor, based on the collected vehicle status data and the obtained navigation map data. More specifically, the vehicle status and navigation map data can be used to determine when the current conditions are suitable for performing calibration. When the current conditions are satisfactory, the calibration factor is calculated. Thereafter, the onboard sensor can be calibrated in response to the current calibration factor.

Abstract: A method for localizing a vehicle in a digital map. GPS raw measurement data is retrieved from satellites. A digital map of a region traveled by the vehicle based on the raw measurement data is retrieved from a database. The digital map includes a geographic mapping of a traveled road and registered roadside objects. The registered roadside objects are positionally identified in the digital map by earth-fixed coordinates. Roadside objects are sensed in the region traveled by the vehicle using distance data and bearing angle data. The sensed roadside objects are matched on the digital map. A vehicle position is determined on the traveled road by fusing raw measurement data and sensor measurements of the identified roadside objects. The position of the vehicle is represented as a function of linearizing raw measurement data and the sensor measurement data as derived by a Jacobian matrix and normalized measurements, respectively.

Abstract: A system and method are provided for operating an autonomous or semi-autonomous host vehicle. The method includes receiving data measured from a plurality of sensors, wherein the measured data relates to one or more target vehicles in the host vehicle's field of view, calculating a desired speed command based on a driver-selected set-speed and the measured data, detecting initiation of a host vehicle lane change to a desired adjacent lane, and in response to initiation of the lane change, selecting an acceleration profile based on at least one set of operating conditions, calculating a modified speed command by adjusting the desired speed command according to the selected acceleration profile; and controlling a host vehicle speed based on the modified speed command.

Abstract: A system and method for selectively reducing or filtering data provided by one or more vehicle mounted sensors before using that data to detect, track and/or estimate a stationary object located along the side of a road, such as a guardrail or barrier. According to one example, the method reduces the amount of data by consolidating, classifying and pre-sorting data points from several forward looking radar sensors before using those data points to determine if a stationary roadside object is present. If the method determines that a stationary roadside object is present, then the reduced or filtered data points can be applied to a data fitting algorithm in order to estimate the size, shape and/or other parameters of the object. In one example, the output of the present method is provided to automated or autonomous driving systems.

Abstract: The methods and system described herein may be used to assist an automated driving system of a host vehicle. The methods and system may, in an exemplary embodiment, be used to determine whether a host vehicle or a target vehicle is cutting out and, accordingly, control the acceleration and/or other driving features of the host vehicle. Generally, the methods described herein contain the steps of determining that a vehicle is cutting out, determining which vehicle is cutting out, and then controlling the acceleration of the host vehicle based on the previous determinations. The determination of which vehicle is cutting out is made based on readings gathered by the host vehicle from one or more automated driving sensors. By using target vehicle sensor data in conjunction with lane marking sensor data, the host vehicle can determine, not only that a vehicle is cutting out, but which vehicle(s) are cutting out.

Abstract: A method is disclosed for improved target grouping of sensor measurements in an object detection system. The method uses road curvature information to improve grouping accuracy by better predicting a new location of a known target object and matching it to sensor measurements. Additional target attributes are also used for improved grouping accuracy, where the attributes includes range rate, target cross-section and others. Distance compression is also employed for improved grouping accuracy, where range is compressed in a log scale calculation in order to diminish errors in measurement of distant objects. Grid-based techniques include the use of hash tables and a flood fill algorithm for improved computational performance of target object identification, where the number of computations can be reduced by an order of magnitude.

Abstract: A method and system for controlling a position of a vehicle relative to other vehicles on a road is provided. The method includes controlling a position of a host vehicle travelling in a lane of a road using an automated driving system and monitoring positions of one or more side vehicles located on either side of the host vehicle and traveling in adjacent lanes. The method further includes detecting a condition in which the host vehicle is or will be positioned in a blind spot of a side vehicle disposed on in an adjacent lane and adjusting the position of the host vehicle in response to the condition using the automated driving system such that the amount of time the host vehicle is or will be positioned in the blind spot of the side vehicle is reduced.

Abstract: A method for localizing a vehicle in a digital map. GPS raw measurement data is retrieved from satellites. A digital map of a region traveled by the vehicle based on the raw measurement data is retrieved from a database. The digital map includes a geographic mapping of a traveled road and registered roadside objects. The registered roadside objects are positionally identified in the digital map by earth-fixed coordinates. Roadside objects are sensed in the region traveled by the vehicle using distance data and bearing angle data. The sensed roadside objects are matched on the digital map. A vehicle position is determined on the traveled road by fusing raw measurement data and sensor measurements of the identified roadside objects. The position of the vehicle is represented as a function of linearizing raw measurement data and the sensor measurement data as derived by a Jacobian matrix and normalized measurements, respectively.

Abstract: A method and system for localizing a vehicle in a digital map includes generating GPS coordinates of the vehicle on the traveled road and retrieving from a database a digital map of a region traveled by the vehicle based on the location of the GPS coordinates. The digital map includes a geographic mapping of a traveled road and registered roadside objects. The registered roadside objects are positionally identified in the digital map by longitudinal and lateral coordinates. Roadside objects in the region traveled are sensed by the vehicle. The sensed roadside objects are identified on the digital map. A vehicle position on the traveled road is determined utilizing coordinates of the sensed roadside objects identified in the digital map. The position of the vehicle is localized in the road as a function of the GPS coordinates and the determined vehicle position utilizing the coordinates of the sensed roadside objects.

Abstract: A method is disclosed for improved target grouping of sensor measurements in an object detection system. The method uses road curvature information to improve grouping accuracy by better predicting a new location of a known target object and matching it to sensor measurements. Additional target attributes are also used for improved grouping accuracy, where the attributes includes range rate, target cross-section and others. Distance compression is also employed for improved grouping accuracy, where range is compressed in a log scale calculation in order to diminish errors in measurement of distant objects. Grid-based techniques include the use of hash tables and a flood fill algorithm for improved computational performance of target object identification, where the number of computations can be reduced by an order of magnitude.

Abstract: A method is provided for estimating vehicle velocity for a vehicle using a single-antenna global positioning system (GPS). An absolute speed and a course angle of the vehicle is measured using the single-antenna GPS. The yaw rates of the vehicle are measured independently of the GPS. An integrated yaw rate of the vehicle is calculated as a function of the measured yaw rates over a period of time. A yaw angle is determined as a function of a reference yaw angle and the integrated yaw rate. Aside slip angle is calculated as a function of the estimated yaw angle and the course angle provided by the GPS. The vehicle velocity is determined as a function of the absolute speed and the side slip angle. The vehicle velocity is provided to a vehicle dynamic control application.

Abstract: Systems, processes, and techniques for calibrating an onboard sensor of a vehicle are presented here. The vehicle has a control system that is capable of performing at least some of the tasks related to the calibration procedure. An exemplary methodology collects vehicle status data and obtains navigation map data during operation of the vehicle. A current calibration factor is calculated for the onboard sensor, based on the collected vehicle status data and the obtained navigation map data. More specifically, the vehicle status and navigation map data can be used to determine when the current conditions are suitable for performing calibration. When the current conditions are satisfactory, the calibration factor is calculated. Thereafter, the onboard sensor can be calibrated in response to the current calibration factor.

Abstract: A method of detecting and tracking objects using multiple radar sensors. Objects relative to a host vehicle are detected from radar data generated by a sensing device. The radar data includes Doppler measurement data. Clusters are formed, by a processor, as a function of the radar data. Each cluster represents a respective object. Each respective object is classified, by the processor, as stationary or non-stationary based on the Doppler measurement data of each object and a vehicle speed of the host vehicle. Target tracking is applied, by the processor, on an object using Doppler measurement data over time in response to the object classified as a non-stationary object; otherwise, updating an occupancy grid in response to classifying the object as a stationary object.

Abstract: A system and method for selectively reducing or filtering data provided by one or more vehicle mounted sensors before using that data to detect, track and/or estimate a stationary object located along the side of a road, such as a guardrail or barrier. According to one example, the method reduces the amount of data by consolidating, classifying and pre-sorting data points from several forward looking radar sensors before using those data points to determine if a stationary roadside object is present. If the method determines that a stationary roadside object is present, then the reduced or filtered data points can be applied to a data fitting algorithm in order to estimate the size, shape and/or other parameters of the object. In one example, the output of the present method is provided to automated or autonomous driving systems.

Abstract: A method of detecting and tracking objects using multiple radar sensors. Objects relative to a host vehicle are detected from radar data generated by a sensing device. The radar data includes Doppler measurement data. Clusters are formed, by a processor, as a function of the radar data. Each cluster represents a respective object. Each respective object is classified, by the processor, as stationary or non-stationary based on the Doppler measurement data of each object and a vehicle speed of the host vehicle. Target tracking is applied, by the processor, on an object using Doppler measurement data over time in response to the object classified as a non-stationary object; otherwise, updating an occupancy grid in response to classifying the object as a stationary object.

Abstract: Methods and systems are provided for measurement association in vehicles. An object proximate a vehicle is identified. Measurements or classifications are obtained via one or more sensors. A first tracking gate is generated that is based at least in part on a characteristic of one of the sensors used to obtain the measurements or classifications. A second tracking gate is generated that is based at least on part on the first tracking gate and a measurement history.

Abstract: A method and system for localizing a vehicle in a digital map includes generating GPS coordinates of the vehicle on the traveled road and retrieving from a database a digital map of a region traveled by the vehicle based on the location of the GPS coordinates. The digital map includes a geographic mapping of a traveled road and registered roadside objects. The registered roadside objects are positionally identified in the digital map by longitudinal and lateral coordinates. Roadside objects in the region traveled are sensed by the vehicle. The sensed roadside objects are identified on the digital map. A vehicle position on the traveled road is determined utilizing coordinates of the sensed roadside objects identified in the digital map. The position of the vehicle is localized in the road as a function of the GPS coordinates and the determined vehicle position utilizing the coordinates of the sensed roadside objects.

Abstract: A system and method is provided for determining a lateral velocity and a longitudinal velocity of a vehicle equipped. The vehicle includes only one antenna for a GPS receiver and a magnetic compass. A magnitude of a velocity vector of the vehicle is determined. A course angle with respect to a fixed reference using the single antenna GPS receiver is determined. A yaw angle of the vehicle is measured with respect to the fixed reference using a magnetic compass. A side slip angle is calculated as a function of the course angle and the yaw angle. The lateral velocity and longitudinal velocity is determined as a function of the magnitude of the velocity vector and the side slip angle.

Abstract: A method of stabilizing a vehicle is provided. The vehicle is travelling at a forward speed and a lateral speed, and comprises a lateral acceleration sensor, a yaw sensor adapted to detect an actual yaw rate of the vehicle around a central axis, a steering mechanism adapted to steer the vehicle by a steered yaw rate, and an electronic stability control system.