Method of misalignment correction and diagnostic function for lane sensing sensor

A method of diagnosing a state of health of a vision-based lane sensing system. A first misalignment factor is calculated as a function of a vehicle lateral offset and a vehicle heading. A second misalignment factor is calculated as a function of a vehicle speed, an estimated curvature of an expected path of travel, a lane curvature, and the vehicle heading. Histograms are generated for the first and second misalignment factors. A probability of a state of health is determined. A determination is made whether the probability of the state of health is within a predetermined threshold. An angle misalignment of the vision system is estimated. The angle misalignment of the vision system is corrected in response to the determination that the probability of the state of health is within the predetermined threshold; otherwise a warning of a faulty lane sensing system is actuated.

BACKGROUND OF INVENTION

An embodiment relates to lane sensing calibration for vision sensors of a lane sensing system.

A lane departure warning system and lane centering system are a couple examples of vehicle systems designed to either warn a driver when the vehicle begins to unintentionally move out of its lane or maintain a vehicle in its lane. Visions systems are one of the devices typically used for sensing the lane of the road of travel. Vision sensors require proper alignment; otherwise, results from the lane sensing system may be skewed. For example, a forward facing image capture device captures a scene exterior of the vehicle in a forward direction. Typically the forward facing capture device would be aligned with a centerline of the vehicle if mounted on the centerline of the vehicle. Any angle misalignment in the vision image device would result inaccurate positioning of the vehicle in the lane which would ultimately hinder the vehicle from properly detecting departure of a lane of an attempt to maintain a vehicle in a center of the lane.

Systems exist where a vehicle vision capture device is manually corrected by bringing the vehicle into a service station where a service personnel determines whether an angle misalignment is present and the service personnel corrects the misalignment manually. What would be beneficial is to have an automated system that autonomously detects a misalignment and can autonomously correct a misalignment.

SUMMARY OF INVENTION

An advantage of an embodiment is an autonomous detection of a misalignment of the image capture device in a lane sensing system and autonomous correction of an angle misalignment. The system utilizes parameters with respect to the vehicle and the road, such as the vehicle yaw rate, vehicle lateral offset, vehicle heading, vehicle speed, lane curvature, and an estimated curvature of an expected path of travel for detecting angle misalignment. Based on determined probabilities, an angle misalignment is determined and the angle misalignment is autonomously corrected if the misalignment is with a respective tolerance. If the misalignment outside of a respective tolerance, then warning is actuated for having the lane sensing system serviced.

An embodiment contemplates a method of diagnosing a state of health of a vision-based lane sensing system for a vehicle. A first misalignment factor is calculated as a function of a vehicle lateral offset and a vehicle heading. A second misalignment factor is calculated as a function of a vehicle speed, an estimated curvature of an expected path of travel of the vehicle, a lane curvature of a traveled road, and the vehicle heading. A histogram is generated for the first misalignment factor and a histogram for the second misalignment factor. A probability of a state of health is determined based on the histogram of first misalignment factor and the histogram of the second misalignment factor. A determination is made whether the probability of the state of health is within a predetermined threshold. An angle misalignment of the vision system is estimated. The angle misalignment of the vision system is corrected in response to the determination that the probability of the state of health is within the predetermined threshold; otherwise a warning of a faulty lane sensing system is actuated.

DETAILED DESCRIPTION

There is shown inFIG. 1a vision angle misalignment system10for detecting a state of health of a vision-based lane sensing system. The vehicle system10analyzes a vision system used by the vehicle such as but not limited to, lane departure warning systems or lane centering systems, for determining whether any misalignment has occurred with respect to the vision system. The vehicle system10includes a vision-based capture device12for capturing images exterior of the vehicle. A processor14receives data obtained by the vision-based capture device12and analyzes the data for determining any substantial violations in the lane sensing system. The vision-based capture device12may be used to detect a vehicle heading θ, vehicle lateral offset y, an estimated curvature of an expected path of travel of the vehicle CY, a lane curvature of a traveled road CL.

A yaw rate sensor16or similar device may be used to determine a yaw rate of the vehicle. A vehicle speed sensor18including by not limited to, wheel speed sensor, PCM, throttle sensor, accelerator pedal sensor, may be used to determine the speed of the vehicle. A buffer20, preferably a circular buffer, may be used to store data including historical data that may be used for determining misalignments in the vision-based capture device12.

The processor14analyzes data from various devices for determining whether a substantial error is present that indicates an anomaly in the vision-based lane sensing system. If the determination is made that the angle misalignment is not a substantial variation from a norm, then a calibration module can automatically calibrate parameters for correcting a minor misalignment via calibration module22. The calibration module22can be a standalone unit or can be integrated as part of the processor or vision-based capture device. If a determination is made that a substantial variation from a norm is present, then a warning device24is actuated for notifying a driver of the fault and that the vehicle needs to be taken to a vehicle service station.

FIG. 2is a graphic illustration of a correlation between a lateral offset of a vehicle and a vehicle heading for detecting angle misalignment of the vision-based imaging device. The vehicle30is shown traveling along the road of travel32at a vehicle speed. A direction in which the vehicle10is driving is designated by a vehicle heading θ. A vehicle lateral offset y is shown and is the distance from the vehicle to the edge of the lane of the road of travel.

InFIG. 3illustrates a block diagram setting forth the mathematical function applied to the various input data for detecting angle measurement in the lane sensing system. The lateral offset y, the vehicle speed v, and vehicle heading θ are each applied as input parameters to the model. A derivative of the vehicle lateral offset y is obtained at block40. The derivative of the vehicle lateral offset y and the host vehicle speed data v are applied as inputs to a division mathematical function42where vehicle lateral offset y is divided by the host vehicle speed data v. The mathematic model for the division function is represented as

The result is then input to the subtraction mathematic function shown generally at44. The mathematic model from the subtraction mathematic function is represented as

In block46, a heading measurement misalignment can be determined and is represented as a change in the vehicle heading with respect to the lane Δθ.

In block48, a heading measurement misalignment is also applied to a distribution estimator where an abnormal detection statistic is analyzed for determining a substantial violation in the lane sensing system. This may be represented by the probability P(|a|<Ta) where Tais the distribution limit from the mean and P|a| represents the probability of whether angle misalignment is within the limits.

FIG. 4is a graphic illustration of a correlation between vehicle heading, vehicle yaw rate, and lane curvature for detecting angle misalignment in the lane sensing system. The vehicle30is shown traveling along the road of travel32having a lane curvature CL. The vehicle is traveling at a vehicle speed v with a vehicle heading θ. A vehicle lateral offset y is shown and is the distance from the vehicle to the edge of the lane of the road of travel.

FIG. 5illustrates a block diagram of the mathematical function applied to the various input data. The vehicle heading θ, the vehicle speed v, the yaw rate ω, and the lane curvature CLare each applied as input parameters to the model.

The vehicle yaw rate ω and the vehicle speed data v are applied as inputs to a division mathematical function50where yaw rate ω is divided by the host vehicle speed data v for generating an estimated curvature of an expected path of travel of the vehicle represented by CY. The mathematic model for the estimated curvature of an expected path of travel is represented as

The result from block50along with the lane curvature CLis applied to a subtraction mathematic function shown generally at52. The mathematic model for the difference mathematic function52is represented as (CY−CL).

At mathematical function54a product of the curvature differences (CY−CL) and the vehicle speed v is determined. The result of the product along with the derivative of the vehicle heading θ is then applied to a subtraction mathematical function56. The resulting mathematic function is represented by the following formula:
β={dot over (θ)}−(CY−CL)vH
where {dot over (θ)} is a derivative of the vehicle heading with respect to a lane of travel, CYis the estimated curvature of an expected path of travel of the vehicle, CLis the lane curvature of a traveled road, and v is the vehicle speed.

In block58, a heading measurement misalignment is determined. A distribution estimator is applied for determining whether an abnormal detection statistic is present. This may be represented by the probability P(|β|<Tβ) where Tβis a determined distribution limit from the mean and P|β| represents the probability of whether angle misalignment is within the limits.

FIG. 6represents a histogram generated for a respective set of data for determining whether the angle misalignment data is within a correctable limit. InFIG. 6, the horizontal axis represents the respective angle measurements whereas the vertical axis represents the number of counts for each respective angle. The limits (thresholds) are identified as −Ta<Δθ and Ta+Δθ where Tarepresents the distribution limits and Δθ represents the angle misalignment offset from the mean of the data of the histogram. Based on the histogram, the probabilities P|α| and P|β| can be determined.

FIG. 7illustrates a flow diagram for determining an angle misalignment in the lane sensing system.

In block60, the routine is initiated. In block61, a determination is made as to whether new sensor data is obtained. If new sensor data is obtained, the routine proceeds to block62; otherwise, the routine waits for new data.

In block62, a determination is made as to whether a lane-cross event is detected. A lane-cross event may be determined by monitoring the vehicle speed for determining whether the speed is greater than a predetermined speed, whether the turn signal is not actuated, and whether no lane-cross event has occurred within a predetermined amount of time, by the vision lane sensing system.

If a determination is made that an intended lane change is occurring, the routine returns to step61to await new sensor data. If an unintended lane change is occurring, then the routine proceeds to step63.

In step63, the deviation in the vision misalignment is determined. If the vision sensors are properly aligned, then

y.vH=θ
and {dot over (θ)}=(CY−CL)vH. If the comparisons are not equal, then the first misalignment factor is determined by

α=y.vH-θ.
and the second misalignment factor is determined by β={dot over (θ)}−(CY−CL)vH.

In block64, histograms are recursively estimated for α and β.

In block65, the probability is determined of whether each set of data is within a misalignment threshold. The probabilities are represented by P(|a|<Ta) and P(|β|<Tβ).

In block66, a probability for a state of health of the vision lane sensing system is determined. The probability for the state of health may be determined by the formula:
P(SOH)=P(|a|<Ta)P(|β|<Tβ)

In block67, a determination is made as to whether the SOH is less than a predetermined SOH threshold (e.g., 0.8). If a determination is made that the probability for the SOH is less than the predetermined threshold, then the routine proceeds to block68where a fault is actuated to a driver of the vehicle indicating a faulty lane sensing system; otherwise the routine proceeds to block69. A SOH implies that the larger probability is, the higher likelihood that the sensors are properly aligned with only minor alignment required. Therefore, when SOH is less than a threshold, a sensing system fault will be reported.

In block69, a misalignment estimation Δθ is determined. The misalignment estimation is utilized to correct the angle misalignment by the vehicle. The routine then proceeds to block61and awaits a next set of data.