Abstract: The present invention provides a fractional order sliding mode synchronous control method for a teleoperation system based on an event trigger mechanism. The method comprises: establishing a dynamics model for the teleoperation system by considering external disturbance and parameter uncertainty, selecting a master robot and a slave robot, interactively establishing the teleoperation system through a communication network, determining system parameters of the dynamics model, designing a fractional order nonsingular rapid terminal sliding mode surface equation by utilizing a position tracking error and a fractional order calculus, setting a trigger event condition of information interaction between the master robot and the slave robot, designing a self-adaptive fractional order nonsingular rapid terminal sliding mode controller based on the sliding mode, designing a Lyapunov function to carry out stability analysis, proving the boundedness of a closed-loop state signal of the system.

Abstract: An autonomous driving system for an autonomous vehicle includes a plurality of on-board autonomous sensors that sense data related to operation of the autonomous vehicle and a surrounding environment and an automated driving controller in electronic communication with the plurality of on-board autonomous sensors. The automated driving controller is instructed to receive an indication one or more of the plurality of on-board autonomous sensors are non-functional and a secondary autonomous sensor system including one or more replacement sensors are installed. The automated driving controller is instructed to verify the secondary autonomous sensor system based on a security check and perform a redundancy check between the one or more replacement sensors and the plurality of on-board autonomous sensors. In response to determining the one or more replacement sensors are valid based on the redundancy check, the automated driving controller operates the autonomous vehicle in a limp home mode.

Abstract: A system for a vehicle includes a plurality of sensors onboard the vehicle and a controller. A first sensor of the plurality of sensors is configured to detect lane markings on a roadway. The controller is configured to store data from the plurality of sensors. In response to receiving an indication indicating a misdetection of lane markings on the roadway based on data received from the first sensor, the controller is configured to execute in parallel a plurality of procedures configured to detect a plurality of causes for the misdetection of lane markings, respectively, based on the stored data; isolate one of the causes as a root cause for the misdetection of lane markings; and provide a response for mitigating the misdetection of lane markings on the roadway based on the root cause for the misdetection of lane markings.

Abstract: A vehicle, and a system and method of navigating the vehicle. The system includes a camera and a processor. The camera obtains an image of a road upon which the vehicle is moving. The processor is configured to extract a feature of the road from the image, perform a lane detection algorithm to detect a set of lane markers in the road using the image and the feature, and move the vehicle along the road by tracking the set of lane markers.

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: 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.

Abstract: A first network device includes a transceiver, a memory and a control module. The transceiver receives an integrated model from a second network device that is separate from the first network device. The memory stores the integrated model and diagnostic trouble code data, most probable cause data, and least probable cause data, which have corresponding cause of issue indications for an issue of a vehicle. The control module while executing the integrated model: compares the cause of issue indications to determine whether the cause of issue indications are consistent such that a same cause of issue is indicated; in response to the cause of issue indications being consistent, displays the same cause of issue, and in response to the cause of issue indications being inconsistent and based on a set of conditions, displays a portion of health related information while refraining from displaying another portion of the health related information.

Abstract: A system for a vehicle includes a plurality of sensors onboard the vehicle and a controller. A first sensor of the plurality of sensors is configured to detect lane markings on a roadway. The controller is configured to store data from the plurality of sensors. In response to receiving an indication indicating a misdetection of lane markings on the roadway based on data received from the first sensor, the controller is configured to execute in parallel a plurality of procedures configured to detect a plurality of causes for the misdetection of lane markings, respectively, based on the stored data; isolate one of the causes as a root cause for the misdetection of lane markings; and provide a response for mitigating the misdetection of lane markings on the roadway based on the root cause for the misdetection of lane markings.

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: A system for monitoring a battery of a vehicle includes a processor and a memory storing instructions which when executed by the processor configure the processor to receive first features including statistics of internal resistances of a plurality of cell groups in a battery pack of the battery, compute second features for the battery pack based on the first features, determine whether the battery pack is faulty based on one or more of the second features, and determine, in response to the battery pack being faulty, whether one or more of the cell groups is faulty based on one or more of the first features.

Abstract: Methods and systems for a remote transportation system including a first autonomous vehicle, at least one second autonomous vehicle and a remote transportation server are provided. The server includes non-transitory computer readable media and one or more processors configured by programming instructions on the non-transitory computer readable media to: receive a request for tow service from the first vehicle, wherein the request includes a location of the first autonomous vehicle; identify a towing destination for the first autonomous vehicle based on the location of the first autonomous vehicle; perform an initial fault diagnosis to determine a towing type; confirm the towing type with the first autonomous vehicle to be at least one of a ground tow, an aerial tow, and a sensor kit tow; and coordinate towing of the first autonomous vehicle to the towing destination by the at least one second autonomous vehicle based on the towing type.

Abstract: Methods and systems for a remote transportation system including a first autonomous vehicle, at least one second autonomous vehicle and a remote transportation server are provided. The at least one second autonomous vehicle includes non-transitory computer readable media and one or more processors configured by programming instructions on the non-transitory computer readable media to: receive a request for tow service from the remote transportation server, wherein the request includes a location of the first autonomous vehicle; locate and identify the first autonomous vehicle based on the request; create a communicate link between the first autonomous vehicle and the second autonomous vehicle; select at least one of a centralized towing method and a projection-based towing method based on the request; and perform autonomous towing of the first autonomous vehicle based on the selected of the at least one of the centralized towing method and the projection-based towing method.

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.

Abstract: An autonomous driving system for an autonomous vehicle includes a plurality of on-board autonomous sensors that sense data related to operation of the autonomous vehicle and a surrounding environment and an automated driving controller in electronic communication with the plurality of on-board autonomous sensors. The automated driving controller is instructed to receive an indication one or more of the plurality of on-board autonomous sensors are non-functional and a secondary autonomous sensor system including one or more replacement sensors are installed. The automated driving controller is instructed to verify the secondary autonomous sensor system based on a security check and perform a redundancy check between the one or more replacement sensors and the plurality of on-board autonomous sensors. In response to determining the one or more replacement sensors are valid based on the redundancy check, the automated driving controller operates the autonomous vehicle in a limp home mode.

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: A first network device includes a transceiver, a memory and a control module. The transceiver receives an integrated model from a second network device that is separate from the first network device. The memory stores the integrated model and diagnostic trouble code data, most probable cause data, and least probable cause data, which have corresponding cause of issue indications for an issue of a vehicle. The control module while executing the integrated model: compares the cause of issue indications to determine whether the cause of issue indications are consistent such that a same cause of issue is indicated; in response to the cause of issue indications being consistent, displays the same cause of issue, and in response to the cause of issue indications being inconsistent and based on a set of conditions, displays a portion of health related information while refraining from displaying another portion of the health related information.

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: The invention provides the internal fixation system of spine posterior screw-plate, including the vertebral plate. The vertebral plate is curved, its internal cambered surface directly faces the spine, and external cambered surface of vertebral plate is equipped with a reinforcing rib. The vertebral plate is set with the perforative injecting hole. One side of vertebral plate is fixed with a fixed connecting plate, and the end of the fixed connecting plate away from the vertebral plate is set with the first regulating hole. The bottom of the fixed connecting plate on two sides of the first regulating hole is set with n-shaped caulking groove.

Abstract: The invention provides the internal fixation system of multi-function adjustable spine posterior screw-rod. It not only includes the vertebral plate, but also includes the adjustable connecting rod, screw and lock nut. Among them, vertebral plate is curved, its internal cambered surface directly faces the spine, and external cambered surface of vertebral plate is equipped with a reinforcing rib. The vertebral plate is set with the perforative injecting hole, and the external cambered surface of vertebral plate is set with the located block. The surface of the located block is set with the concave threaded hole, and the located block on two sides of the threaded hole is set with the U-shaped bracket. The top of screw expands to form a locking block, which surface is set with the concave locking hole. The locking block on both sides of locking hole is set with the U-shaped locking groove.

Abstract: A control system for identifying and responding to effective system changes in a monitored system includes data collection, change identification, classifier construction, effective system change and parameter adjustment modules. The data collection module tracks parameters of the monitored system. The change identification module, based on the parameters, identifies: during training, performance changes and first system changes; and post training, a set of performance changes and second system changes. The classifier construction module, based on the performance changes and the first system changes construct a performance-system change classifier module. The performance-system change classifier module, based on the set of performance changes, determines possible system changes and respective probability values. The effective system change module, based on the second and possible system changes, determines effective system changes and respective probability values.