Gm Global Technolgoy Operations, Inc.

Qi Zhang

Qingrong Zhao

Bakhtiar Brian Litkouhi

Jinsong Wang

Wende Zhang

Xinyu Du

Shengbing Jiang

Yingmei Si

Nikolai K Moshchuk

Youssef A Ghoneim

Bo Yu

Wen-Chiao Lin

David M Sidlosky

Ibrahim A Badiru

Sami Ahmed

Aaron B Bloom

Alexander M Allan

Yadollah Sabri

Valarie V Boatman

Steven T Schweitzer

Simon E Zimmerman

Christopher C Au

Ping Hwang

A system comprises a processor and a memory storing instructions which when executed by the processor configure the processor to estimate a rack force for a steering system of a vehicle based on current driving and environmental conditions and estimate a health of an actuator of the steering system of the vehicle. The instructions configure the processor to estimate maximum achievable angle, velocity, and acceleration for the actuator of the steering system based on the estimated rack force and the estimated health of the actuator. The instructions configure the processor to provide to the steering system a path planned for the vehicle based on the estimated maximum achievable angle, velocity, and acceleration for the actuator of the steering system.

Real-time estimation of achievable angle, velocity, and acceleration capabilities of steering actuator

An automotive vehicle includes an actuator configured to control vehicle steering, a sensor configured to detect a yaw rate of the vehicle, and a controller. The controller is configured to estimate a yaw rate and lateral velocity of the vehicle via a vehicle dynamics model based on a measured longitudinal velocity of the vehicle, calculated road wheel angles of the vehicle, and estimated tire slip angles of the vehicle. The controller is configured to receive a measured yaw rate from the sensor, and to calculate a difference between the measured yaw rate and the estimated yaw rate. The controller is configured to apply a model correction to the vehicle dynamics model using a PID controller based on the difference, and to estimate a vehicle position based on the estimated lateral velocity and the measured longitudinal velocity. The controller is configured to automatically control the actuator based on the vehicle position.

System and method for control of an autonomous vehicle

Methods and systems are provided for evaluating a CAN that includes a CAN bus and a plurality of modules configured to communicate over the CAN bus. A voltage sensor may be provided in electrical communication with the CAN bus. A number (N) of pairs of voltages may be read. Each pair may include a CAN high (CAN-H) value and a CAN low (CAN-L) value. The N pair of voltages may be processed through a comparison of the CAN-H values and the CAN-L values. Whether a fault signature is present in the CAN-H and CAN-L values may be determined from the processing.

Fault isolation for a controller area network

A method for determining wetness on a path of travel. A surface of the path of travel is captured by at least one image capture device. A plurality of wet surface detection techniques is applied to the at least one image. An analysis for each wet surface detection technique is determined in real-time of whether the surface of the path of travel is wet. Each analysis independently determines whether the path of travel is wet. Each analysis by each wet surface detection technique is input to a fusion and decision making module. Each analysis determined by each wet surface detection technique is weighted within the fusion and decision making module by comprehensive analysis of weather information, geology information, and vehicle motions. A wet surface detection signal is provided to a control device. The control device applies the wet surface detection signal to mitigate the wet surface condition.

Fusion-based wet road surface detection

Methods and system are provided for monitoring a controller area network (CAN) bus. In one embodiment, a method includes: receiving fault data associated with the CAN bus; processing, by a processor, the fault data with a software based method to determine a first propositions set; processing, by the processor, the fault data with a hardware based method to determine a second proposition set; processing, by the processor, the first propositions set and the second proposition set to determine a fault decision; and generating, by the processor, a diagnosis of the CAN bus based on the fault decision.

Methods and systems for diagnosing a controller area network

Methods and systems for determining road surface information in a vehicle. In one embodiment, the method includes: determining at least one condition assessment value based on steering data; determining a feature set to include at least one of self-aligning torque (SAT), slip angle, SAT variance, steering rate, and lateral acceleration based on the condition assessment value; processing steering data obtained during a steering maneuver and associated with the feature set using a pattern classification technique; and determining a surface type based on the processing.

Methods and systems for estimating road surface friction coefficient using self aligning torque

Methods and systems are provided for determining a road surface friction coefficient and controlling a feature of the vehicle based thereon. In one embodiment, a method includes: receiving signals from an electronic power steering system and an inertial measurement unit; estimating parameters associated with an electronic power steering system model using an iterative optimization method; calculating an electronic power steering system variable using the electronic power steering system model, the estimated parameters and one or more of the received signals; determining whether the calculated electronic power steering system variable satisfies a fitness criterion; and when the calculated electronic power steering system variable does satisfy the fitness criterion, determining a road surface friction coefficient based on at least one of the estimated parameters.

Methods and systems for estimating road surface friction

A method for determining a thickness of water on a path of travel. A plurality of images of a surface of the path of travel is captured by an image capture device over a predetermined sampling period. A plurality of wet surface detection techniques are applied to each of the images. A detection rate is determined in real-time for each wet surface detection technique. A detection rate trigger condition is determined as a function of a velocity of the vehicle for each detection rate. The real-time determined detection rate trigger conditions are compared to predetermined detection rate trigger conditions in a classification module to identify matching results pattern. A water film thickness associated with the matching results pattern is identified in the classification module. A water film thickness signal is provided to a control device. The control device applies the water film thickness signal to mitigate the wet surface condition.

Vision-based on-board real-time estimation of water film thickness

A method for determining a snow covered surface condition of a path of travel. A beam of light is emitted at a surface of the path of travel by a light emitting source. An image of a path of travel surface is captured by an image capture device. The image capture device is mounted on the vehicle and captures an image in a downward direction. The captured image captures the beam of light emitted on the path of travel surface. Analyzing a subsurface scattering of the light generated on the path of travel surface by a processor. A determination is made whether snow is present on the path of travel. A snow covered path of travel surface signal is generated in response to the identification of snow on the path of travel.

Snow covered path of travel surface condition detection

A method for determining a wet surface condition of a road. An image of a road surface is captured by an image capture device of the host vehicle. The image capture device is mounted on a side of the host vehicle and captures an image in a downward direction. Identifying in the captured image, by a processor, a region of interest. The region of interest is in a region sideways to a face of the wheel. The region of interest is representative of where sideways splash as generated by the wheel occurs. A determination is made whether water is present in the region of interest. A wet road surface signal is generated in response to the identification of water in the region of interest.

Vision-based wet road surface condition detection using tire side splash

A method for determining a wet surface condition of a road. Capturing an image of a wheel of a remote vehicle traveling in an adjacent lane by an image capture device of a host vehicle. Identifying in the captured image, by processor of a host vehicle, a region of interest relative to the wheel where the region of interest is representative of where precipitation dispersion occurs. A determination is made whether precipitation is present in the region of interest. A wet road surface signal is generated in response to the identification of precipitation in the adjacent lane.

Wet road surface condition detection

A method for determining a wet surface condition of a road. An image of a road surface is captured by an image capture device of the host vehicle. The image capture device is mounted on a side of the host vehicle and captures an image in a downward direction. A region of interest rearward of the wheel of the host vehicle is identified in the captured image by a processor. The region of interest is representative of where rearward splash as generated by the wheel occurs. A determination is made whether precipitation is present in the region of interest by applying a filter to the image. A wet road surface signal is generated in response to the identification of precipitation in the region of interest.

Vision-based wet road surface condition detection using tire rearward splash

A method of determining a wet surface condition of a road. An image of a road surface is captured by an image capture device of the host vehicle. The image capture device is mounted on a side of the host vehicle and an image is captured in a downward direction. A region of interest is identified in the captured image by a processor. The region of interest is in a region rearward of a tire of a host vehicle. The region of interest is representative of where a tire track as generated by the tire rotating on the road when the road surface is wet. A determination is made whether water is present in the region of interest as a function of identifying the tire track. A wet road surface signal is generated in response to the identification of water in the region of interest.

Vision-based wet road surface condition detection using tire tracks

Systems and methods use an image of a road surface that is generated by a camera. The image includes a pattern from a light source. A region of interest in the image is determined based on the pattern from the light source. A total area is determined that includes at least part of the region of interest and an area adjacent the region of interest. A feature vector is extracted based on the region of interest and the total area. A road condition is determined based on the feature vector and a classifier.

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Sterling Heights, MI, United States of America

Qi Zhang