Abstract:
A controller area network (CAN) on a mobile system has a plurality of CAN elements including a communication bus and nodes. A method for monitoring the CAN includes detecting inactive nodes of the CAN and employing an off-board controller to identify a candidate fault in the CAN based upon the inactive nodes of the CAN and a network topology for the CAN. A fault is isolated in the CAN based upon the candidate fault.
Abstract:
A controller area network (CAN) has a plurality of CAN elements including a communication bus and controllers. A method for monitoring the CAN includes identifying each of the controllers as one of an active controller and an inactive controller. A fault-active controller isolation process is executed to detect and isolate presence of a fault-active controller. A fault isolation process can be executed to detect and isolate presence of one of a wire open fault, a wire short fault and a controller fault when one of the controllers is identified as an inactive controller. Presence of a fault associated with a persistent bus disturbance in the CAN is detected when a bus error count is greater than a predetermined threshold continuously for a predetermined period of time.
Abstract:
A vehicle, system method for operating the vehicle is disclosed. The system includes a camera and a processor. The camera is configured to obtain a camera image of a road segment. The processor determines a location of a road edge for the road segment within the camera image, obtains a lane attribute for the road segment, generates a virtual lane mark for the road segment based on the road edge and the lane attribute, and moves the vehicle along the road segment by tracking the virtual lane mark.
Abstract:
A process for local traffic approximation through analysis of cloud data is provided. The process includes, within a computerized traffic flow estimation controller of a host vehicle, operating programming to monitor a planned navigational route of the host vehicle, identify along the planned navigational route a road section including cross-traffic, monitor cloud data related to a mobile cellular device, analyze the cloud data to identify traffic posing a hazardous condition to the host vehicle within the road section, and generate a vehicle alert to a driver of the host vehicle based upon the identified traffic.
Abstract:
A system includes a first most probable cause (MPC) module, a second MPC module, and an integrated MPC module. The first MPC module is configured to determine a first most probable cause of an issue on a vehicle based on at least one service procedure for the vehicle. The second MPC module is configured to determine a second most probable cause of the issue based on repair data for other vehicles. The integrated MPC module is configured to determine an integrated most probable cause of the issue based on the first and second most probable causes.
Abstract:
A method and system of diagnosing and suggesting least probable faults for an exhibited vehicle failure. The method includes initiating a vehicle health management (VHM) algorithm to monitor a state of health (SOH) for at least one vehicle component at each vehicle operating event over a predetermined time period. The VHM algorithm determines at least one of a Green SOH, a Yellow SOH, and a Red SOH designation with a confidence level for the at least one vehicle component; calculating a number of Green SOH designations (Ncalculated) over the predetermined time period; and upon an exhibited vehicle failure, providing a least probable cause indication for the at least one component when a set of conditions are met. The set of conditions includes (i) Ncalculated is equal to or greater than a predetermined number of Green SOH designations and (ii) no Yellow SOH and Red SOH designations are present.
Abstract:
A method and system of diagnosing and suggesting least probable faults for an exhibited vehicle failure. The method includes initiating a vehicle health management (VHM) algorithm to monitor a state of health (SOH) for at least one vehicle component at each vehicle operating event over a predetermined time period. The VHM algorithm determines at least one of a Green SOH, a Yellow SOH, and a Red SOH designation with a confidence level for the at least one vehicle component; calculating a number of Green SOH designations (Ncalculated) over the predetermined time period; and upon an exhibited vehicle failure, providing a least probable cause indication for the at least one component when a set of conditions are met. The set of conditions includes (i) Ncalculated is equal to or greater than a predetermined number of Green SOH designations and (ii) no Yellow SOH and Red SOH designations are present.
Abstract:
An autonomic vehicle control system includes a perception module of a spatial monitoring system that is disposed to monitor a spatial environment proximal to the autonomous vehicle. A method for evaluating vehicle dynamics operation includes determining a desired trajectory for the autonomous vehicle, wherein the desired trajectory includes desired vehicle positions including an x-position, a y-position and a heading. Vehicle control commands are determined based upon the desired trajectory, and include a commanded steering angle, an acceleration command and a braking command. Actual vehicle states responsive to the vehicle control commands are determined. An estimated trajectory is determined based upon the actual vehicle states, and a trajectory error is determined based upon a difference between the desired trajectory and the estimated trajectory. The trajectory error is monitored over a time horizon, and a first state of health (SOH) is determined based upon the trajectory error over the time horizon.
Abstract:
A method for use with a vehicle having one or more subsystems includes receiving vehicle health management (VHM) information via a controller indicative of a state of health of the subsystem. The VHM information is based on prior testing results of the subsystem. The method includes determining a required testing profile using the testing results, applying the testing profile to the subsystem to thereby control a state of the subsystem, and measuring a response of the subsystem to the applied testing profile. The method also includes recording additional testing results in memory of the controller that is indicative of a response of the subsystem to the applied testing profile. The vehicle includes a plurality of subsystems and a controller configured to execute the method.
Abstract:
A perception module of a spatial monitoring system to monitor and characterize a spatial environment proximal to an autonomous vehicle is described. A method for evaluating the perception module includes capturing and storing a plurality of frames of data associated with a driving scenario for the autonomous vehicle, and executing the perception module to determine an actual spatial environment for the driving scenario, wherein the actual spatial environment for the driving scenario is stored in the controller. The perception module is executed to determine an estimated spatial environment for the driving scenario based upon the stored frames of data associated with the driving scenario, and the estimated spatial environment is compared to the actual spatial environment for the driving scenario. A first performance index for the perception module is determined based upon the comparing, and a fault can be detected.