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.
Abstract:
A method for diagnosing a fault mode in a system includes recording a hierarchical precedence rule assigning a priority level to fault modes of the system, and recording, in a fault report matrix, fault reports indicative of a corresponding one or more of the fault modes. The method also includes using the hierarchical precedence rule to determine the assigned relative priority level for the fault reports in response to a predetermined condition, e.g., a requested engine starting event, and identifying a root cause subsystem as a subsystem having the highest assigned priority level. A control action executed via the controller identifies the root cause subsystem by recording a diagnostic code and/or transmitting a message. The system is also disclosed, as is a computer-readable medium programmed with instructions embodying the method.
Abstract:
A scheduling controller in communication with a plurality of autonomous vehicles is described, and includes an operator request compiler, a fleet state-of-health database, an environmental conditions compiler and a fleet scheduling controller. The fleet scheduling controller is configured to deploy the autonomous vehicles based upon inputs from the operator request compiler, the fleet state-of-health database and the environmental conditions compiler. A process for coordinating a fleet of autonomous vehicles includes determining states of health for the autonomous vehicles, and determining a desired autonomous vehicle use requirement from each of a plurality of operators that are associated with the autonomous vehicles. A usage schedule for each of the autonomous vehicles is determined based upon the states of health and the desired autonomous vehicle use requirements from the operators. The autonomous vehicles are deployed based upon the usage schedule.
Abstract:
A method for monitoring controller area network (CAN) on a mobile system includes identifying links and associated nodes between all the nodes of the CAN, and ranking all the links according to their order of connection to the monitoring controller, including assigning lower ranks to ones of the links proximal to the monitoring controller and assigning higher ranks to ones of the links distal to the monitoring controller. For each of said links, the associated node distal to the monitor is identified. The on-board monitoring controller determines a fault signature for each of the links starting with the link having the highest ranking, said fault signature comprising identified ones of the associated nodes distal to the monitor for each of the corresponding links.
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 controller processes data from one or more sensors of a subsystem of a vehicle. The processing includes smoothing the data and calculating a mean of the data. The controller identifies a transition point in the processed data where a moving average of the data is less than the mean by a predetermined amount indicating a trend. The controller selects a segment of the processed data subsequent to the transition point, detects the trend in the segment using regression, and extrapolates the detected trend to reach a predetermined fault threshold. The controller predicts a failure of the subsystem based on a slope of the extrapolated trend and provides an indication of the prediction based on the slope to schedule a service for the subsystem.
Abstract:
An autonomous driving system for an autonomous vehicle includes an automated driving controller wirelessly connected to a towing taxi. The automated driving controller determines the autonomous driving system is non-functional. In response to determining the autonomous driving system is non-functional, the automated driving controller generates a notification indicating the autonomous driving system is non-functional. The automated driving controller receives, from the towing taxi, a current data string including a data point corresponding to a current point in time in combination with a predicted data point for each of one or more predicted points of time in the future. The current data string is compared with a previous data string recorded at a previous point in time. In response to determining the current data string matches the previous data string, the automated driving controller determines one or more driving maneuvers for the autonomous vehicle based on the current data string.