Patent Publication Number: US-2022234603-A1

Title: Vehicle Lateral-Control System with Adjustable Parameters

Description:
BACKGROUND 
     A Society of Automotive Engineers (SAE) Level 2 automated-driving system includes driver-assistance features that provide steering, braking, and acceleration assistance, for example, lane centering and adaptive cruise control. Vehicles equipped with Level 2 automated-driving systems require a human driver to be poised to take control of the vehicle in the event the automated-driving system relinquishes control. Factory settings of lateral-control parameters for automated-driving systems are calibrated based on an assumption that drivers will feel comfortable with the steering maneuvers performed by the system in an automated-driving mode. However, some drivers may consider the factory-set automated-steering maneuvers to feel unnatural, too aggressive for traffic conditions, or not aggressive enough for their liking. As a result, some drivers may deactivate the automated-driving mode, which can lead to reduced vehicle safety. 
     SUMMARY 
     This document describes one or more aspects of a vehicle lateral-control system with adjustable parameters. In one example, the system includes a controller circuit configured to receive, from a driver-monitoring sensor, identity data indicating an identity of a driver of a vehicle. The controller circuit receives, from one or more vehicle sensors, vehicle lateral-response data based on steering maneuvers performed as the vehicle operates under control of the driver. The controller circuit determines, based on the vehicle lateral-response data, a plurality of lateral-steering parameters of the vehicle. The controller circuit adjusts, based on the plurality of lateral-steering parameters, lateral-control parameters of the vehicle. The controller circuit associates the adjusted lateral-control parameters of the vehicle with the identity of the driver. The controller circuit operates the vehicle according to the lateral-control parameters associated with the identity of the driver. 
     In another example, a method includes receiving from a driver-monitoring sensor, with a controller circuit, identity data indicating an identity of a driver of a vehicle. The method includes receiving from one or more vehicle sensors, with the controller circuit, vehicle lateral-response data based on steering maneuvers performed as the vehicle operates under control of the driver. The method includes determining, based on the vehicle lateral-response data, with the controller circuit, a plurality of lateral-steering parameters of the vehicle. The method includes adjusting based on the plurality of lateral-steering parameters, with the controller circuit, lateral-control parameters of the vehicle. The method includes associating the adjusted lateral-control parameters of the vehicle with the identity of the driver. The method includes operating the vehicle, with the controller circuit, according to the lateral-control parameters associated with the identity of the driver. 
     This summary is provided to introduce aspects of a vehicle lateral-control system with adjustable parameters, which is further described below in the Detailed Description and Drawings. For ease of description, the disclosure focuses on vehicle-based or automotive-based systems, such as those that are integrated on vehicles traveling on a roadway. However, the techniques and systems described herein are not limited to vehicle or automotive contexts but also apply to other environments where sensors can be used to determine dynamics of a moving body. This summary is not intended to identify essential features of the claimed subject matter, nor is it intended for use in determining the scope of the claimed subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The details of one or more aspects of a vehicle lateral-control system with adjustable parameters are described in this document with reference to the following drawings. The same numbers are used throughout the drawings to reference like features and components: 
         FIG. 1  illustrates an example of a vehicle lateral-control system with adjustable parameters shown mounted on a vehicle, in accordance with techniques of this disclosure; 
         FIG. 2  illustrates an example driver-monitor sensor isolated from the example of a vehicle lateral-control system with adjustable parameters of  FIG. 1 ; 
         FIG. 3  illustrates an example of vehicle sensors of the vehicle lateral-control system with adjustable parameters of  FIG. 1 ; 
         FIG. 4  illustrates an example of the vehicle lateral-control system with adjustable parameters of  FIG. 1  with a driver-facing camera; 
         FIG. 5  illustrates an example of a data flow of the vehicle lateral-control system with adjustable parameters of  FIG. 4 ; 
         FIG. 6  illustrates an example of vehicle lateral-response data for a vehicle traveling on a roadway, in accordance with techniques of this disclosure; 
         FIG. 7  illustrates examples of lateral-steering parameters and lateral-control parameters of the vehicle lateral-control system with adjustable parameters of  FIG. 4 ; 
         FIG. 8  illustrates an example of a human machine interface (HMI) of the vehicle lateral-control system with adjustable parameters of  FIG. 4 ; 
         FIG. 9  illustrates another example of a human machine interface (HMI) of the vehicle lateral-control system with adjustable parameters of  FIG. 4 ; 
         FIG. 10  illustrates yet another example of a human machine interface (HMI) of the vehicle lateral-control system with adjustable parameters of  FIG. 4 ; 
         FIG. 11  is a flow chart illustrating an example lateral-control parameters that are adjusted based on the HMI inputs, in accordance with techniques of this disclosure; 
         FIG. 12  is an example logic flow of the vehicle lateral-control system with adjustable parameters of  FIG. 1 ; and 
         FIG. 13  is an example method of operating the example of the vehicle lateral-control system with adjustable parameters of  FIG. 1 . 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     The techniques of this disclosure relate to a vehicle lateral-control system with adjustable parameters. A controller circuit receives data from in-cabin sensors that indicate an identity of a driver while the driver is operating a vehicle in a manual-driving mode. The system learns the driver&#39;s steering habits or behaviors that affect a lateral response of the vehicle, for example, how aggressively the driver steers into and out of curves or positions the vehicle relative to lane markings and adjacent vehicles. The system associates or matches the learned steering behavior with the identity of the driver and adjusts lateral-control parameters that are used to control the vehicle when the vehicle is operated in an autonomous-driving mode. The system can store the adjusted lateral-control parameters for different drivers in a memory and recall the adjusted parameters when the driver&#39;s identity is recognized by the system. The driver can further adjust an aggressiveness of the lateral-control parameters by inputting a preference into a human machine interface (HMI) that can be presented to the driver on a console display of the vehicle. The HMI can include preset and adjustable selections that relate to lane biasing and corner-cutting on curves. The vehicle lateral-control system with adjustable parameters can improve driver and passenger comfort by reproducing the driver&#39;s steering behavior when the vehicle is operating in the autonomous-driving mode, resulting in an improved user experience. 
     Example System 
       FIG. 1  illustrates an example of a vehicle lateral-control system with adjustable parameters  100 , hereafter referred to as the system  100 . The system  100  includes a controller circuit  102  configured to receive identity data  104  from a driver-monitor sensor  106 , indicating an identity of a driver of a vehicle  108 . The driver-monitor sensor  106  can be a component of an occupant-monitor system  110  (OMS  110 ) installed on the vehicle  108  that monitors some or all occupants or passengers inside the vehicle cabin. The controller circuit  102  receives vehicle lateral-response data  112  based on steering maneuvers performed as the vehicle  108  operates under control of the driver, for example, as the driver steers the vehicle on a roadway and executes various turns or lane changes. The vehicle lateral-response data  112  is received from vehicle sensors  114  that directly or indirectly detect or measure lateral movement or lateral accelerations of the vehicle  108 . For example, a difference between wheel speeds detected by left and right wheel-speed sensors can indirectly indicate the vehicle  108  is turning, compared to a yaw-rate sensor that directly measures an angular rotation of the vehicle  108 . 
     The controller circuit  102  can determine, based on the vehicle lateral-response data  112 , lateral-steering parameters  116  that can be used to tune or adjust lateral-control parameters  118  of the vehicle  108 . The lateral-steering parameters  116  represent a processing of the raw vehicle lateral-response data  112  and can be more readily used by the controller circuit  102  to match the driver&#39;s steering behavior when operating the vehicle  108  in the autonomous-driving mode. The lateral-control parameters  118  of the vehicle  108  are calibratable or tunable parameters that can be interpreted by vehicle controls  120  to control the steering, braking, and acceleration of the vehicle  108 . 
     The controller circuit  102  can adjust the lateral-control parameters  118  of the vehicle  108  and associate the adjusted lateral-control parameters  118  with the identity of the driver in the memory of the controller circuit  102 . The controller circuit  102  can recall the adjusted lateral-control parameters  118  for a particular driver from the memory when the vehicle  108  is operated in the autonomous-driving mode and the driver is identified or recognized while seated in the driver&#39;s seat. 
     Although the vehicle  108  can be any vehicle, for ease of description, the vehicle  108  is primarily described as being a self-driving automobile that is configured to operate in the autonomous-driving mode to assist the driver riding onboard the vehicle  108 . The vehicle  108  can be capable of SAE Level 2 autonomous operation (as referred to in the Background) that assists the driver with steering, braking, and acceleration, while the driver monitors the operation of the vehicle  108  at all times from a driver&#39;s seat. 
     In the example illustrated in  FIG. 1 , the controller circuit  102  is installed on the vehicle  108  and is communicatively coupled to the driver-monitor sensor  106 , the vehicle sensors  114 , and the vehicle controls  120  via transmission links. The transmission links can be wired or wireless interfaces, for example, BLUETOOTH®, Wi-Fi®, near field communication (NFC), universal serial bus (USB), universal asynchronous receiver/transmitter (UART), or controller area network (CAN). In some examples, the controller circuit  102  receives data from other vehicle systems via a CAN bus (not shown), for example, an ignition status and a transmission gear selection. 
     Controller Circuit 
     The controller circuit  102  may be implemented as a microprocessor or other control circuitry such as analog and/or digital control circuitry. The control circuitry may include one or more application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) that are programmed to perform the techniques, or one or more general-purpose hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. The controller circuit  102  may also combine custom hard-wired logic, ASICs, or FPGAs with custom programming to perform the techniques. The controller circuit  102  may include a memory or storage media (not shown), including non-volatile memory, such as electrically erasable programmable read-only memory (EEPROM) for storing one or more routines, thresholds, and captured data. The EEPROM stores data and allows individual bytes to be erased and reprogrammed by applying programming signals. The controller circuit  102  may include other examples of non-volatile memory, such as flash memory, read-only memory (ROM), programmable read-only memory (PROM), and erasable programmable read-only memory (EPROM). The controller circuit  102  may include volatile memory (e.g., dynamic random-access memory (DRAM), static random-access memory (SRAM)). The controller circuit  102  can include one or more clocks or timers used to synchronize the control circuitry or determine an elapsed time of events. The one or more routines may be executed by the processor to perform steps for operating the vehicle  108  based on signals received by the controller circuit  102  from the driver-monitor sensor  106  and the vehicle sensors  114  as described herein. 
     Driver-Monitor Sensor 
       FIG. 2  illustrates an example of the driver-monitor sensor  106  that is located remotely from the system  100 . The driver-monitor sensor  106  is configured to monitor the driver of the vehicle  108 , as will be described in more detail below. The driver-monitor sensor  106  can include one or more sensors that detect aspects of the driver and can be components of the OMS  110  installed on the vehicle  108 . The driver-monitor sensor  106  can include a camera that captures images of the driver, and the OMS  110  determines whether the driver&#39;s seat is occupied by a person based on the images. The camera can be a two-dimensional (2D) camera  106 - 1  or a 3D time-of-flight camera  106 - 2  that measures a time for light pulses to leave the camera and reflect back on the camera&#39;s imaging array. 
     Software executed by the OMS  110  can distinguish persons from animals and objects using known image-analysis techniques. The objects in the images are detected in regions of interest that correspond to the seating positions (e.g., the driver&#39;s seat position) within the cabin, and the objects are classified by the software into human and other classifications. Processing blocks or models in the software are pre-trained to recognize human forms or shapes of other objects, for example, a shopping bag, a box, or an animal. 
     The driver-monitor sensor  106  can also include a steering-wheel-torque sensor  106 - 3  that detects a torque applied to the steering wheel. The torque can be detected when the driver places a hand on the steering wheel, even with the autonomous control system steering the vehicle  108 . The steering-wheel-torque sensor  106 - 3  can be an electro-mechanical device integrated into a power-steering system of the vehicle  108  that determines a torsion bar angle required for the steering movement. The steering-wheel-torque sensor  106 - 3  can also output a steering angle and rate of change of the steering wheel angular position. 
     The driver-monitor sensor  106  can also include a seat-pressure sensor  106 - 4  that detects a pressure or pressure distribution applied to the seat (similarly, a steering-wheel-pressure sensor may be used to detect a pressure or pressure distribution applied by a driver&#39;s hands on the steering-wheel). The OMS  110  can determine whether the driver is occupying the driver&#39;s seat (or holding the steering-wheel) based on a pressure threshold indicative of a weight of the driver (or force exerted with a grip of the steering-wheel). For example, if the weight of the occupant is greater than thirty kilograms, the OMS  110  may determine that the driver is considered an adult. The pressure distribution can indicate whether the object occupying the driver&#39;s seat is a person or an object other than a person. The pressure distribution can also indicate whether the driver is in the correct position within the driver&#39;s seat; for example, when the driver is leaning over to one side of the seat, the pressure is concentrated on one side of the seat. 
     The driver-monitor sensor  106  can also include a capacitive steering-wheel sensor  106 - 5  that detects a touch of the driver&#39;s hands on the steering wheel. The capacitive steering-wheel sensors  106 - 5  can be located in a rim of the steering wheel and can detect contact points of the hands with the steering wheel. In some examples, touching the steering wheel with the hands distorts an electric field generated by the sensor and changes a capacitance of the sensor, indicating the presence of the driver&#39;s hand. The capacitive steering-wheel sensor  106 - 5  can detect whether one or both driver&#39;s hands are on the steering wheel. 
     The driver-monitor sensor  106  can also include a radar sensor  106 - 6  that detects a presence of objects in the vehicle cabin, and the OMS  110  can determine whether the driver&#39;s seat is occupied by the driver or the object based on point cloud data received from the radar sensor  106 - 6 , and may detect whether the driver&#39;s hands are on the steering-wheel. The OMS  110  compares the point cloud data to models in the software to determine whether the seat is occupied by the person or the object. In some examples, the radar sensor  106 - 6  can detect relatively small movements, for example, movements of a chest wall of the driver that is breathing (e.g., a child in a car seat). 
     The driver-monitor sensor  106  can also include a microphone  106 - 7  that detects a voice of the driver, and the OMS  110  can use voice-recognition software to process voice recordings to determine the identifying features that are unique to the voice of the driver. The microphone  106 - 7  can be a component of an infotainment system of the vehicle  108 . 
     The driver-monitor sensor  106  can also include a capacitive fingerprint sensor  106 - 8  that detects a fingerprint of the driver, and the OMS  110  can use fingerprint-recognition software to process the fingerprints to determine the identifying features that are unique to the detected fingerprints of the driver. 
     The OMS  110  and controller circuit  102  can use machine learning to detect the various driver aspects and steering behaviors. Machine learning is a data-analytics technique that teaches computers to learn from experience. Machine learning routines, or algorithms, use computational methods to learn information from data without relying on a predetermined equation as a model. The routines improve their performance as the sample size available for learning increases. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. 
     Supervised learning uses classification and regression techniques to develop predictive models. Common algorithms for performing classification include support vector machine (SVM), boosted and bagged decision trees, k-nearest neighbor, Naïve Bayes, discriminant analysis, logistic regression, and neural networks. Common regression algorithms include linear model, nonlinear model, regularization, stepwise regression, boosted and bagged decision trees, neural networks, and adaptive neuro-fuzzy learning. Unsupervised learning finds hidden patterns or intrinsic structures in data and is used to draw inferences from datasets consisting of input data without labeled responses. Clustering is a common unsupervised learning technique. Common algorithms for performing clustering include k-means and k-medoids, hierarchical clustering, Gaussian mixture models, hidden Markov models, self-organizing maps, fuzzy c-means clustering, and subtractive clustering. In the context of self-driving automobiles, the OMS  110  and controller circuit  102  can use machine learning specifically to determine, based on the driver-monitor sensor  106 , the identity of the driver or other aspects of driving behavior that feed the vehicle lateral-response data  112  to ensure the controller circuit  102  can accurately determine the lateral-steering parameters  116 . 
     Vehicle Sensors 
       FIG. 3  illustrates examples of the vehicle sensors  114  that are located remotely from the system  100 . The vehicle sensors  114  can include an inertial measurement unit (IMU)  114 - 1 , a steering-angle sensor  114 - 2 , a vehicle-speed sensor  114 - 3 , a localization sensor  114 - 4 , external-facing cameras  114 - 5 , and ranging sensors  114 - 6 . 
     The IMU  114 - 1  is an electronic device that detects a relative movement of the vehicle  108  and can include a yaw rate, a longitudinal acceleration, a lateral acceleration, a pitch rate, and a roll rate of the vehicle  108 . The IMU  114 - 1  can use a combination of accelerometers and gyroscopes to detect the relative motion of the vehicle and can be a component of a dynamic control system installed on the vehicle  108 . 
     The steering-angle sensor  114 - 2  can be a component of the steering-wheel-torque sensor  106 - 3  that outputs a steering angle and rate of change of the steering wheel&#39;s angular position, as described above. 
     The vehicle-speed sensor  114 - 3  can be a rotational sensor (e.g., a wheel-speed sensor) where the signal may also be used to operate a speedometer of the vehicle  108 . The vehicle-speed sensor  114 - 3  can also use data from a global navigation satellite system (GNSS) that may be a component of the navigation system installed on the vehicle  108 , for example, a global positioning system (GPS) that determines the speed based on a change in positions of the vehicle  108 . 
     The localization sensor  114 - 4  can be the GNSS. GNSS refers to a constellation of satellites that transmit signals from space that provide positioning and timing data to GNSS receivers located on the vehicle  108 . The receivers then use this data to determine a location of the vehicle  108 . GNSS provides positioning, navigation, and timing services on a global or regional basis. GPS, BeiDou, Galileo, and GLONASS, IRNSS, and QZSS are examples of GNSS operated by the USA, the People&#39;s Republic of China, the European Union, India, and Japan, respectively. 
     The external-facing cameras  114 - 5  can be video cameras that capture images of the roadway traveled by the vehicle  108  or objects proximate to the vehicle  108 . The images can include lane markings that define borders of the roadway or travel lanes and other vehicles. The controller circuit  102  can classify the objects in the images using software to identify the objects. 
     The ranging sensors  114 - 6  can be radar sensors, LiDAR sensors, or ultrasonic sensors that detect objects proximate to the vehicle  108  that may be components of an advanced driver-assistance system (ADAS) that may be installed on the vehicle  108 . Radar sensors use radio frequency (RF) signals to detect objects and can determine distances based on a time to send and receive a reflected RF signal or a travel time of the RF signal. The radar sensor can also detect movement of the object based on a change in a phase of the reflected RF signal known as the Doppler effect. LiDAR sensors operate in a similar manner to radar sensors but instead use laser light pulses to detect objects and distances based on the travel time of the laser pulse and the Doppler effect. Ultrasonic sensors use the travel time of sound waves to detect objects and distances. 
     Image-Based Identification 
       FIG. 4  illustrates an example of the system  100  with a driver-facing 2D camera  106 - 1  capturing images of the driver. The 2D camera  106 - 1  is configured to detect identifying features of a face of the driver of the vehicle  108 . For example, the 2D camera  106 - 1  detects features unique to the driver that can be used to distinguish the driver from other passengers in the vehicle  108  or other drivers that may operate the vehicle  108 . 
     The 2D camera  106 - 1  can capture an image of the face of the driver, and the OMS  110  can process the image to determine one or more facial features that are unique to the driver. The OMS  110  can use facial-recognition techniques that involve storing a digital image of the driver&#39;s face in a memory of the OMS  110 . The facial-recognition techniques enable the OMS  110  to pinpoint and measure the facial features captured by the image, for example, a distance between two features (e.g., two parts of a mouth, two ears, two eyes, two pupil centers), a position of a feature (e.g., a placement of a nose relative to other facial features), or a shape of a feature (e.g., a face, a brow, a jaw-line). These measured facial features can be determined by the OMS  110  and retained the memory of the OMS  110  for later use by the system  100 , as will be explained in more detail below. The OMS  110  can determine and store the identities of multiple drivers, and the identity data  104  from the OMS  110  can be periodically updated by the OMS  110  to ensure the controller circuit  102  can accurately associate the driver&#39;s identity with the adjusted lateral-control parameters  118 . For example, the OMS  110  can update the identity data  104  at ten-second intervals to account for driver changes during stops. 
     Driver Steering Behavior Learning 
       FIG. 5  is a flowchart  500  illustrating an example of the types of vehicle lateral-response data  112  that can be used by the system  100  to learn the driver&#39;s steering behavior.  FIG. 5  illustrates data flows from the vehicle sensors that feed the determination of the lateral-steering parameters  116 . 
     At  502 , upon vehicle ignition, the system identifies the driver using the 2D camera  106 - 1 , as described above and illustrated in  FIG. 4 . At  504 , the controller circuit  102  determines whether data from the vehicle sensors  114  is available. At  506 , the system  100  delays the learning process and defaults to factory-installed lateral-control parameters  118  if data from the vehicle sensors  114  is not available. The factory-installed lateral-control parameters  118  are initial lateral-control parameters  118  that are not associated with a driver&#39;s identity. If data from the vehicle sensors  114  is available, the system  100  proceeds with the learning process, as will be described in more detail below. 
       FIG. 6  illustrates the vehicle  108  traveling in the travel lane indicated by lane markings on the left side and right side of the vehicle  108 . In this example, the centerline of the travel lane is determined by the forward-facing camera based on the images of the lane markings. The vehicle lateral-response data  112  includes a cross-track error  112 - 1  relative to a lane centerline, a heading error  112 - 2  relative to a reference point or a look-ahead point  122 , and a roadway curvature  112 - 3  that are detected by the external-facing camera  114 - 5  or the localization sensor  114 - 4 . The cross-track error  112 - 1  indicates a difference between a coordinate center  124  of the vehicle  108  relative to a closest point on the centerline of the travel lane. In the example illustrated in  FIG. 6 , the coordinate center  124  of the vehicle  108  is a point at a center of a front bumper. In this example, a positive cross-track error  112 - 1  indicates the coordinate center  124  of the vehicle  108  is positioned to a right side of the centerline of the travel lane, and a negative cross-track error  112 - 1  indicates the coordinate center  124  is positioned to a left side of the centerline. The cross-track error  112 - 1  can be measured in units of distance as detected by the external-facing camera  114 - 5  or detected by the localization sensor  114 - 4 . The roadway curvature  112 - 3  is defined as an inverse of the radius of curvature of the roadway and can be determined by the forward-facing camera or the position from the localization sensor  114 - 4  in relation to a digital map that includes data for the roadway curvature  112 - 3 . 
     In the example illustrated in  FIG. 6 , the heading error  112 - 2  indicates the deviation in a heading  126  or pointing direction of the vehicle  108  relative to a reference point or the look-ahead point  122  on the centerline of the travel lane ahead of the vehicle  108 . In some examples, the heading error  112 - 2  indicates the deviation between an orientation of the vehicle  108  and a tangent vector to the centerline of the travel lane. The heading error  112 - 2  can be measured in angular units, and a heading error  112 - 2  of zero degrees indicates that the driver is steering the vehicle  108  directly to the look-ahead point  122 . The distance between the vehicle coordinate center  124  and the look-ahead point  122  is a look-ahead distance  128 , and the controller circuit  102  can change the look-ahead distance  128  based on the roadway curvature  112 - 3 . For example, as the roadway curvature  112 - 3  decreases approaching a straight road, the controller circuit  102  can increase the look-ahead distance  128  because the steering corrections needed to keep the vehicle  108  centered are smaller than for the roadway with a tighter curve or larger roadway curvature  112 - 3 . 
     Referring back to  FIG. 5 , the vehicle lateral-response data  112  can also include a turn-reaction time  112 - 4  that can be determined based on the roadway curvature  112 - 3 , the vehicle speed from the vehicle-speed sensor  114 - 3 , and the yaw rate from the IMU  114 - 1 . The turn-reaction time  112 - 4  indicates the driver&#39;s steering reaction when entering a curve from a straight section of roadway or when encountering a change in the curvature of the roadway. 
     At  508 , the controller circuit  102  compares the roadway-curvature rate to the turning rate or yaw rate of vehicle  108  to determine the turn-reaction time  112 - 4 . The roadway-curvature rate can be calculated by the controller circuit  102  by multiplying the roadway curvature  112 - 3  by the velocity of the vehicle  108 . The controller circuit  102  can subtract the yaw rate from the roadway-curvature rate and compare the result to a threshold to determine whether the driver has steered the vehicle in response to the changing roadway curvature  112 - 3 . For example, the driver steering the vehicle  108  traveling at 14 meters per second (m/s), or about 50 kilometers per hour (km/h), entering a curve having a radius of curvature of 100 meters (e.g., a roadway curvature of 0.01/m) will need to turn the vehicle  108  at a rate of about 0.14 radians/s to follow the roadway. If the yaw rate is close to zero (indicating that the driver has not turned the vehicle  108 ), the difference between the roadway-curvature rate and the yaw rate will be 0.14 radians/s. The controller circuit  102  can determine the turn-reaction time  112 - 4  by recording the time between when the vehicle  108  should turn, based on the roadway-curvature rate, and the time when the vehicle actually turns, based on the yaw rate of the vehicle  108 . The controller circuit  102  can compare the turn-reaction time  112 - 4  to a threshold that indicates the driver has steered the vehicle  108  to follow the curve when the turn-reaction time  112 - 4  falls below the threshold. This threshold can be user-defined and can vary with the speed of the vehicle. 
     The vehicle lateral-response data  112  can also include a roadway curvature during a lane change  112 - 5  and a lane-change time  112 - 6 . The roadway curvature during a lane change  112 - 5  can be used to determine the driver&#39;s preference for making lane change maneuvers based on the roadway curvature  112 - 3 . For example, one driver may be comfortable changing lanes on tight curves, while another driver may not be comfortable changing lanes on any curved roads, regardless of the curvature. The lane-change time  112 - 6  is the time for the driver to steer the vehicle  108  from a current travel lane into an adjacent travel lane. The controller circuit  102  can use a lateral velocity derived from the lateral acceleration from the IMU  114 - 1  to determine the time for the driver to make the lane change and can use a vehicle turn signal as a trigger for the calculation. For example, when the driver activates the turn signal to indicate a future lane change, at  510 , the controller circuit  102  can integrate or perform a summation of the lateral distance moved by the vehicle  108  based on the signals received from the IMU  114 - 1 . At  512 , the controller circuit  102  determines whether the lateral distance moved by the vehicle  108  matches the lane width. If the lateral distance moved by the vehicle  108  does not match the lane width, the controller circuit  102  continues to integrate the lateral distance until the vehicle  108  has moved the lateral distance approximating the lane width. The result of this lateral-distance integration can be used by the controller circuit  102  to determine the roadway curvature during a lane change  112 - 5  and the lane-change time  112 - 6 . 
     The vehicle lateral-response data  112  can also include a lateral distance to adjacent vehicles  112 - 7 . The controller circuit  102  can use data from the ranging sensors  114 - 6  or external-facing cameras  114 - 5  to determine the distance the driver places between the vehicle  108  and adjacent vehicles while traveling on the roadway. The lateral distance to adjacent vehicles  112 - 7  can be used by the controller circuit  102  to determine a lane-biasing parameter, as will be described below. 
     Lateral-Steering Parameters 
     Referring again to  FIG. 5 , the controller circuit  102  processes the raw vehicle lateral-response data  112  to determine several lateral-steering parameters  116  that include a root-mean-square (RMS) lane biasing  116 - 1 , an average cross-track error  116 - 2 , an RMS heading error  116 - 3 , an RMS turn-reaction time  116 - 4 , a corner-cutting percentage  116 - 5 , a minimum roadway-curvature enabling a lane change  116 - 6 , and an RMS lane-change time  116 - 7 . In this example, the controller circuit  102  processes much of the vehicle lateral-response data  112  using the RMS technique, or the square root of the arithmetic mean of the squares of the individual data values. The processed vehicle lateral-response data  112  is used to reduce a computational load on the controller circuit  102  that can occur if only the raw vehicle lateral-response data  112  was used in place of the lateral-steering parameters  116 . Other techniques of processing the raw data may be used, for example, an arithmetic mean or running average of the data points. Processing the data using the RMS is used for some parameters because the RMS provides a sense of the magnitude of the numbers in a data set without the negative values offsetting the positive values, as can occur when using the arithmetic mean. As a result, the RMS can be the same or slightly larger than the arithmetic mean. 
     Referring back to  FIG. 5 , at  514 , the controller circuit  102  determines whether the average cross-track error  116 - 2  is a positive or negative value and further determines an overshoot or undershoot of the corner-cutting percentage  116 - 5 , where the positive value indicates the overshoot or a steering path biased to the outside of the curves and the negative value indicates the undershoot or the steering path biased to the inside of the curves. The controller circuit  102  can determine the percentage of overshoot or undershoot based on the average cross-track error  116 - 2  and one half of the lane width. 
     At  516 , the controller circuit  102  determines whether the driver has performed a sufficient number of steering maneuvers for the system  100  to learn the driver&#39;s steering behaviors, and if so, at  518 , moves to adjust the settings of the lateral-control parameters  118  stored in the memory. If the driver has not performed enough steering maneuvers, the controller circuit  102  continues to collect the vehicle lateral-response data  112  from the vehicle sensors  114  until a data threshold is reached for the learning process to be completed. The data threshold can be determined through experimentation for each lateral-steering parameter  116  and may be in a range of thirty to sixty steering events. 
     Lateral-Control Parameters 
       FIG. 7  is a flow chart  700  that illustrates an example of the lateral-control parameters  118  determined by the controller circuit  102  based on the lateral-steering parameters  116 . The lateral-control parameters  118  include a lane-biasing value  118 - 1 , a cross-track-error gain  118 - 2 , a heading-error gain  118 - 3 , a look-ahead-distance gain  118 - 4 , a corner-cutting value  118 - 5 , and lane-change-activation threshold and duration  118 - 6 . 
     At  702 , the controller circuit  102  determines whether the lateral-steering parameters  116 - 1  to  116 - 7  are within their respective predetermined factory-set limits or ranges established by the vehicle manufacturer to ensure the system  100  does not use learned values that may create a vehicle-handling safety issue. For example, the driver that participates in vehicle racing events where the steering behavior may not be suitable for driving the vehicle  108  on the roadway in traffic. If any of the lateral-steering parameters  116 - 1  to  116 - 7  are outside of the manufacturer&#39;s predetermined limits, the controller circuit  102  defaults to the factory settings for the respective lateral-steering parameter  116 . If the lateral-steering parameters  116  are within predetermined limits, the controller circuit  102  proceeds to adjust the lateral-control parameters  118 , ensuring the adjusted lateral-control parameters  118  remain within the predetermined range. 
     The controller circuit  102  can pass through the RMS lane-biasing parameter  116 - 1  to become the lane-biasing value  118 - 1  that indicates the lateral distance the driver places between the vehicle  108  and other vehicles that are traveling in adjacent lanes. 
     At  704 , the controller circuit  102  compares the average cross-track error  116 - 2  to a look-up table stored in the memory. In the example illustrated in  FIG. 7 , the look-up table is used to reduce computational loads on the controller circuit  102 . In other examples, the controller circuit  102  can compute the gain without the look-up table or access the gain from a cloud storage facility that may be accessed by the controller circuit  102  via the infotainment system of the vehicle  108 . The controller circuit  102  can interpolate the cross-track-error gain  118 - 2  based on the values in the look-up table and store the adjusted gain in the memory. 
     At  706 , the controller circuit  102  compares the RMS heading error  116 - 3  to a heading gain look-up table, interpolates the heading-error gain  118 - 3 , and stores the updated gain in the memory. 
     At  708 , the controller circuit  102  compares the RMS turn-reaction time  116 - 4  to a look-ahead gain look-up table, interpolates the look-ahead-distance gain  118 - 4 , and stores the updated gain in the memory. 
     At  710 , the controller circuit  102  converts the corner-cutting percentage  116 - 5  to a distance based on the current lane width and compares this distance to a threshold. The controller circuit  102  activates the corner-cutting when the corner-cutting distance is above the threshold. The threshold can be user-defined and can be based on the vehicle speed. For example, a threshold of 0.02 meters would allow the activation of corner-cutting when the corner-cutting distance exceeds 0.02 meters. The controller circuit  102  then stores the updated corner-cutting value  118 - 5  in the memory. 
     The controller circuit  102  can pass through the minimum roadway curvature, enabling a lane change  116 - 6  and the RMS lane-change time  116 - 7  to generate the lane-change-activation threshold and duration  118 - 6 . The controller circuit can set the curvature threshold to the minimum curvature value based on the minimum roadway curvature enabling a lane change  116 - 6  and can set the lane-change duration based on the RMS lane-change time  116 - 7 . 
     The controller circuit  102  can store the adjusted lateral-control parameters  118  in the memory and associate the adjusted values with the identity of the driver that is also stored in the memory. When the vehicle  108  is being operated in the autonomous-driving mode, the controller circuit  102  can recognize the driver in the driver&#39;s seat and recall the adjusted lateral-control parameters  118  from the memory for the recognized driver. The controller circuit  102  can then operate the vehicle  108  in the autonomous-driving mode using these recalled values to reproduce the driver&#39;s steering habits. 
     Human Machine Interface (HMI) 
       FIGS. 8-10  illustrate examples of a human machine interface  130  (HMI  130 ) that can receive input from the driver indicating the driver&#39;s preference for a lateral-control aggressiveness while the vehicle  108  is operated in the autonomous-driving mode. The examples shown in  FIGS. 8-10  are not intended to encompass all the possible lateral-control input scenarios and are shown to illustrate the concept for receiving input from the driver. The HMI  130  can be presented to the driver on a console display of the vehicle  108  or as an application on a mobile device, for example, a mobile phone or tablet that is synchronized with the vehicle  108 . The HMI  130  can include inputs for preset selections  132  and adjustable selections  134  that provide the driver with the opportunity to further customize their riding experience when the vehicle  108  is operated in the autonomous-driving mode. 
     In the example illustrated in  FIG. 8 , the HMI  130  presents selections for a lane-change aggressiveness that indicates the driver&#39;s preference for a minimum road-curvature to enable a lane change  116 - 6 . The controller circuit  102  can further adjust the lateral-control parameters  118  associated with the identity of the driver that are stored in the memory and used to operate the vehicle  108  in the autonomous-driving mode. In this example, the driver can select from the two preset selections  132  (e.g., sport or comfort) or can select from the adjustable selections  134  by moving a slider bar between positions A and B. In this example, the sporty selection can indicate the driver prefers lane changes on roadways with more curvature compared to the comfort selection where the driver prefers lane changes on roadways with less curvature. The selections of either A or B on the slider bar provide the driver with a visual concept for the curvature of the roadway, with selection A being less aggressive than selection B. The controller circuit  102  can adjust the lateral-control parameters  118  that relate to the driver&#39;s input, for example, adjusting the previously stored lane-change activation threshold and duration  118 - 6  that was determined via the driver learning process. 
       FIG. 9  illustrates an example where the HMI  130  presents selections for a lane-centering aggressiveness that indicates the driver&#39;s preference for lane centering on curves. In this example, the sporty selection can indicate the driver&#39;s preference for steering closer to the inside of curves, while the comfort selection can indicate the driver&#39;s preference for steering closer to the outside of curves where the centrifugal forces perceived by the driver may be reduced. The slider bar offers the driver additional customization options for the lane-centering aggressiveness, as illustrated by positions A, B, and C. The controller circuit  102  can adjust the lateral-control parameters  118  that relate to the driver&#39;s selection, for example, adjusting the lane-biasing value  118 - 1  or the cross-track-error gain  118 - 2 . 
       FIG. 10  illustrates an example where the HMI  130  presents selections for a lane-bias aggressiveness that indicates the driver&#39;s preference for positioning the vehicle  108  in the lane adjacent to parked cars. In this example, the small bias selection can indicate the driver&#39;s preference for driving closer to the line of parked cars, while the large bias selection can indicate the driver&#39;s preference for keeping farther away from the parked cars. The slider bar offers the driver additional customization options for the lane-bias aggressiveness, as illustrated by positions A, B, and C. The controller circuit  102  can adjust the lateral-control parameters  118  that relate to the driver&#39;s selection, for example, adjusting the lane-biasing value  118 - 1 . 
     The controller circuit  102  can adjust the stored lateral-control parameters  118  based on values from look-up tables associated with the selections of lateral-control aggressiveness, as illustrated in the flow chart  1100  in  FIG. 11 . The process for adjusting the lateral-control parameters  118  based on the look-up tables is the same as described above and illustrated in  FIG. 7 . At  1102 , the controller circuit compares the HMI inputs to the factory-set limits to ensure the system  100  does not use learned values that may create a vehicle handling safety issue. At  1104  to  1114 , the controller circuit compares the HMI inputs to look-up tables or predetermined thresholds to further adjust the lateral-control parameters  118 - 1  to  118 - 4 , and  118 - 6 . 
       FIG. 12  is a flow diagram illustrating an example logic flow  1200  performed by the controller circuit  102 . The logic flow starts at  1202  with vehicle ignition and ends at  1216  with driver learning. In this example, at  1202 , upon the driver actuating a vehicle ignition switch inside the vehicle  108 , the controller circuit  102  receives the identity data  104  from the OMS  110 , as described above. At  1204 , the controller circuit  102  determines whether the driver is recognized. If the controller circuit  102  does not recognize the driver, at  1206 , the controller circuit  102  adds the new driver based on the identity data  104  and changes the lateral-control parameters  118  to the default factory settings that are not associated with a driver&#39;s identity. 
     If the controller circuit  102  recognizes the driver, at  1208 , the controller circuit  102  determines whether previous lateral-control parameters  118  are stored in the memory. If no lateral-control parameters  118  are stored in the memory, at  1206 , the controller circuit  102  adds the new driver based on the identity data  104  and changes the lateral-control parameters  118  to the default factory settings. If lateral-control parameters  118  are stored in the memory, at  1210 , the controller circuit  102  recalls the previous lateral-control parameters  118  from the memory that are associated with the recognized driver. 
     At  1212 , the controller circuit  102  determines whether input via the HMI  130  has been received. If input via the HMI  130  has been received, at  1214 , the controller circuit  102  adjusts the lateral-control parameters  118  based on the HMI input. If input via the HMI  130  has not been received, at  1216 , the controller circuit  102  proceeds with the driver learning process as illustrated in  FIGS. 5 and 7  and adjusts the lateral-control parameters  118  as described above. 
     Other Example Identification Techniques 
     The examples described above are related to cameras detecting an image of the face of the driver. In other examples, the system  100  can be configured with other inputs that detect other identifying features that can be used to determine the driver&#39;s identity. The examples below describe other sensors that can detect other identifying features of the driver. The system architecture and logic flows are similar to the examples illustrated in  FIG. 4 , except for the different sensors and corresponding control circuitry to operate the different sensors. 
     Voice-Based Identification 
     In this example, microphones installed on the vehicle  108  detect the voice of the driver. The OMS  110  can use voice-recognition software to process voice recordings to determine the identifying features that are unique to the detected voices and generate feature vectors based on these identifying features. In some examples, the voice-recognition software uses a text-dependent approach where a passphrase spoken by the driver is compared to a recording of the passphrase stored in the memory of the OMS  110 . In other examples, the voice-recognition software uses a text-independent approach where the driver can speak freely to the system  100 , and the software learns the driver&#39;s speech utterances over time. The identifying features of the feature vector can include various components extracted from an acoustic wave speech signal, for example, amplitudes and frequencies from particular bandwidths, formant frequencies or resonances in the frequency spectrum, pitch contours or variations in a fundamental frequency, and coarticulation in which the speech organs prepare to produce the next sound while transitioning from a previous sound. 
     Fingerprint-Based Identification 
     In this example, capacitive fingerprint sensors installed on the vehicle  108  (e.g., on the steering wheel or ignition switch) can detect the fingerprint of the driver. The OMS  110  can use fingerprint-recognition software to process the fingerprints to determine the identifying features that are unique to the detected fingerprints and generate feature vectors based on these identifying features. The identifying features of the feature vector can include various components extracted from the fingerprint, for example, ridge endings and ridge bifurcations. 
     Eye-Based Identification 
     In this example, the cameras are infrared cameras (IR cameras), and the eyes are illuminated with light in the near-IR spectrum by IR illuminators located in the vehicle cabin. In some examples, the OMS  110  can use iris-recognition software that processes images of the iris of one or both eyes. In other examples, the OMS  110  can use retinal recognition software that processes images of the retina of one or both eyes. The identifying features of the feature vector can include various components extracted from the patterns of the iris or retina using known methods of feature extraction, for example, Gabor filters to extract frequency content, discrete wavelet transform (DWT), discrete cosine transform (DCT), or Harr wavelet transform (HWT). 
     Example Method 
       FIG. 13  illustrates example methods  1300  performed by the system  100 . For example, the controller circuit  102  configures the system  100  to perform operations  1302  through  1312  by executing instructions associated with the controller circuit  102 . The operations (or steps)  1302  through  1312  are performed but not necessarily limited to the order or combinations in which the operations are shown herein. Further, any of one or more of the operations may be repeated, combined, or reorganized to provide other operations. 
     Step  1302  includes RECEIVE IDENTITY DATA. This can include the controller circuit  102  receiving identity data  104  from the driver-monitor sensor  106 , indicating the identity of the driver of the vehicle  108 . The driver-monitor sensor  106  may be a component of the OMS  110 , as described above. The identity data  104  can be image-based, voice-based, fingerprint-based, or eye-based data, and the identity data  104  can be stored in the memory of the controller circuit  102  for multiple drivers. 
     Step  1304  includes RECEIVE VEHICLE LATERAL-RESPONSE DATA. This can include the controller circuit  102  receiving vehicle lateral-response data  112  from vehicle sensors  114  based on steering maneuvers performed as the vehicle  108  operates under control of the driver, as described above. The vehicle sensors  114  can directly or indirectly detect lateral movement of the vehicle  108 . The vehicle lateral-response data  112  can be used by the system  100  to learn the driver&#39;s steering behavior and can include the cross-track error  112 - 1  relative to a lane centerline, the heading error  112 - 2  relative to the look-ahead point  122 , the roadway curvature  112 - 3 , the turn-reaction time  112 - 4 , the roadway curvature during a lane change  112 - 5 , the lane-change time  112 - 6 , and the lateral distance to adjacent vehicles  112 - 7 , as described above. 
     Step  1306  includes DETERMINE LATERAL-STEERING PARAMETERS. This can include the controller circuit  102  determining the lateral-steering parameters  116  based on the vehicle lateral-response data  112 , as described above. The controller circuit  102  processes the raw vehicle lateral-response data  112  to determine the lateral-steering parameters  116 . The lateral-steering parameters  116  include the RMS lane biasing  116 - 1 , the average cross-track error  116 - 2 , the RMS heading error  116 - 3 , the RMS turn-reaction time  116 - 4 , the corner-cutting percentage  116 - 5 , the minimum roadway-curvature enabling a lane change  116 - 6 , and the RMS lane-change time  116 - 7 , as described above. 
     Step  1308  includes ADJUST LATERAL-CONTROL PARAMETERS. This can include the controller circuit  102  adjusting the lateral-control parameters  118  that are stored in the memory based on the lateral-steering parameters  116  of the identified driver. The lateral-control parameters  118  are used to control the vehicle  108  when the vehicle  108  is operating in the autonomous-driving mode and can reproduce the driver&#39;s steering behavior. The lateral-control parameters  118  include the lane-biasing value  118 - 1 , the cross-track-error gain  118 - 2 , the heading-error gain  118 - 3 , the look-ahead-distance gain  118 - 4 , the corner-cutting value  118 - 5 , and the lane-change-activation threshold and duration  118 - 6 , as described above. The controller circuit  102  adjusts the lateral-control parameters  118  within a predetermined range established by the vehicle manufacturer to ensure safe vehicle handling. The controller circuit  102  stores the adjusted lateral-control parameters  118  in the memory for later recall, as described above. 
     The controller circuit  102  can further adjust the lateral-control parameters  118  that are stored in the memory via inputs from the HMI  130 , as described above. The HMI  130  can include preset selections  132  and adjustable selections  134  that further enable the driver to customize their experience when operating the vehicle  108  in the autonomous-driving mode. 
     Step  1310  includes ASSOCIATE ADJUSTED PARAMETERS WITH DRIVER IDENTITY. This can include the controller circuit  102  associating or matching the adjusted lateral-control parameters  118  stored in the memory with the identity of the driver that is also stored in the memory, as described above. The controller circuit  102  can associate several driver&#39;s identities with their respective adjusted lateral-control parameters  118  in the memory of the controller circuit  102  and recall the adjusted lateral-control parameters  118  upon recognizing the driver of the vehicle  108 . 
     Step  1312  includes OPERATE VEHICLE. This can include the controller circuit  102  operating the vehicle  108  in the autonomous-driving mode using the lateral-control parameters  118  stored in the memory that are associated with the driver&#39;s identity to reproduce the driver&#39;s steering habits, as described above. 
     EXAMPLES 
     In the following section, examples are provided. 
     Example 1. A system comprising a controller circuit configured to: receive, from a driver-monitoring sensor, identity data indicating an identity of a driver of a vehicle; receive, from one or more vehicle sensors, vehicle lateral-response data based on steering maneuvers performed as the vehicle operates under control of the driver; determine, based on the vehicle lateral-response data, a plurality of lateral-steering parameters of the vehicle; adjust, based the plurality of lateral-steering parameters, lateral-control parameters of the vehicle; associate the adjusted lateral-control parameters of the vehicle with the identity of the driver; and operate the vehicle according to the lateral-control parameters associated with the identity of the driver. 
     Example 2. The system of the previous example, wherein the controller circuit is further configured to: refrain from operating the vehicle according to initial lateral-control parameters associated with the vehicle in response to operating the vehicle according to the lateral-control parameters associated with the identity of the driver of the vehicle, the initial lateral-control parameters being different than the lateral-control parameters associated with the identity of the driver. 
     Example 3. The system of any of the previous examples, wherein the controller circuit is further configured to store a plurality of adjusted vehicle lateral-control parameters associated with a plurality of driver identities in a memory of the controller circuit, the plurality of driver identities including the identity of the driver. 
     Example 4. The system of any of the previous examples, wherein the system further includes a human machine interface (HMI) configured to receive input from the driver indicating a lateral-control aggressiveness, and wherein the controller circuit is further configured to adjust, based on the driver input, the vehicle lateral-control parameters associated with the identity of the driver. 
     Example 5. The system of any of the previous examples, wherein the HMI includes inputs of one or more of preset selections and adjustable selections. 
     Example 6. The system of any of the previous examples, wherein the preset and adjustable selections include one or more of a lane bias relative to stationary vehicles on a roadway, a lane centering on curves, and a minimum road-curvature to enable a lane change. 
     Example 7. The system of any of the previous examples, wherein the controller circuit is further configured to adjust the stored vehicle lateral-control parameters based on values from look-up tables associated with the selections of lateral-control aggressiveness. 
     Example 8. The system of any of the previous examples, wherein the lateral-response data includes one or more of a cross-track error relative to a lane center, a heading error relative to a reference point, a turn-reaction time, a roadway curvature during a lane change, a lane-change time, and a lateral distance to adjacent vehicles. 
     Example 9. The system of any of the previous examples, wherein the lateral-steering parameters include one or more of a root mean square (RMS) lane biasing, an average cross-track error, an RMS heading error, an RMS turn-reaction time, a corner-cutting percentage, a minimum roadway curvature enabling a lane change, and an RMS lane-change time. 
     Example 10. The system of any of the previous examples, wherein the lateral-control parameters include one or more of a lane-biasing value, a cross-track-error gain, a heading-error gain, a look-ahead distance gain, a corner-cutting value, and lane change activation threshold and duration. 
     Example 11. The system of any of the previous examples, wherein the controller circuit is further configured to adjust the lateral-control parameters within a predetermined range. 
     Example 12. The system of any of the previous examples, wherein the one or more sensors include an inertial measurement unit (IMU), a steering angle sensor, a vehicle speed sensor, a localization sensor, a camera, and a ranging sensor. 
     Example 13. A method, comprising: receiving from a driver-monitoring sensor, with a controller circuit, identity data indicating an identity of a driver of a vehicle; receiving from one or more vehicle sensors, with the controller circuit, vehicle lateral-response data based on steering maneuvers performed as the vehicle operates under control of the driver; determining based on the vehicle lateral-response data, with the controller circuit, a plurality of lateral-steering parameters of the vehicle; adjusting based on the plurality of lateral-steering parameters, with the controller circuit, lateral-control parameters of the vehicle; associating the adjusted lateral-control parameters of the vehicle with the identity of the driver; and operating the vehicle, with the controller circuit, according to the lateral-control parameters associated with the identity of the driver. 
     Example 14. The method of the previous example, further comprising refraining from operating the vehicle according to initial lateral-control parameters associated with the vehicle in response to operating the vehicle according to the lateral-control parameters associated with the identity of the driver of the vehicle, the initial lateral-control parameters being different than the lateral-control parameters associated with the identity of the driver. 
     Example 15. The method of any of the previous examples, further comprising storing, with the controller circuit, a plurality of adjusted vehicle lateral-control parameters associated with a plurality of driver identities in a memory of the controller circuit. 
     Example 16. The method of any of the previous examples, further comprising: receiving, via a human machine interface (HMI), input from the driver indicating a lateral-control aggressiveness; and adjusting, with the controller circuit, the stored vehicle lateral-control parameters based on the driver input. 
     Example 17. The method of any of the previous examples, wherein the HMI includes inputs of one or more of preset selections and adjustable selections. 
     Example 18. The method of any of the previous examples, wherein the preset and adjustable selections include one or more of a lane bias relative to stationary vehicles on the roadway, a lane centering on curves, and a minimum road-curvature to enable a lane change. 
     Example 19. The method of any of the previous examples, further comprising adjusting the stored vehicle lateral-control parameters based on values from look-up tables associated with the selections of lateral-control aggressiveness. 
     Example 20. The method of any of the previous examples, wherein receiving the lateral-response data includes receiving one or more of a cross-track error relative to a lane center, a heading error relative to a reference point, a turn-reaction time, a roadway curvature during a lane change, a lane-change time, and a lateral distance to adjacent vehicles. 
     CONCLUSION 
     While various embodiments of the disclosure are described in the foregoing description and shown in the drawings, it is to be understood that this disclosure is not limited thereto but may be variously embodied to practice within the scope of the following claims. From the foregoing description, it will be apparent that various changes may be made without departing from the spirit and scope of the disclosure as defined by the following claims. 
     The use of “or” and grammatically related terms indicates non-exclusive alternatives without limitation unless the context clearly dictates otherwise. As used herein, a phrase referring to “at least one of” a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination with multiples of the same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b, a-c-c, b-b, b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b, and c).