Method for controlling automated driving operation of a motor vehicle

Abstract

A method for controlling the automated driving operation of a motor vehicle crossing a first and a second road segment, comprising: receiving sensor signals indicative of road surface conditions of the first and second road segments from a sensor device via a vehicle control unit of the motor vehicle; determining first and second road friction values ​​of the first and second road segments, respectively, via the vehicle control unit based on the received sensor signals; determining whether the first road friction value differs from the second road friction value; and, in response to the fact that the first road friction value differs from the second road friction value, determining whether the first road friction value is greater or less than the second road friction value.Executing a first vehicle control action with a propulsion and / or braking system of the motor vehicle via the vehicle control system in response to the first road friction value being greater than the second road friction value; and executing a second vehicle control action with a propulsion and / or braking system of the motor vehicle via the vehicle control system in response to the first road friction value being less than the second road friction value; wherein executing the first vehicle control action includes: determining a desired vehicle speed at a transition point where the road surface conditions change from the first road friction value to the second road friction value; determining a desired braking distance for the motor vehicle to achieve the desired vehicle speed at the transition point;and transmitting a control signal with a desired acceleration to a vehicle powertrain and / or vehicle braking system to achieve the desired vehicle speed within the desired braking distance at the transition point; wherein the method further comprises: determining whether the desired acceleration is a deceleration value greater than a maximum permissible deceleration value; and transmitting a warning message to a driver of the motor vehicle indicating that the deceleration value is unacceptably high in response to the fact that the deceleration value is greater than the maximum permissible deceleration value.

Description

Technical FieldThe present disclosure relates generally to motor vehicles having automated driving capabilities. More particularly, aspects of this disclosure relate to automated driving and warning functions for vehicles, such as adaptive cruise control systems and systems for avoiding forward collisions, where distance control is improved for transitional surface conditions.IntroductionCurrent production automobiles, such as the modern automobile, are originally equipped or retrofitted with a network of onboard electronic devices that provide automated driving functions that help minimize driver effort to include. In automotive applications, for example, the cruise control system is the most well known type of automated driving functions. The cruise control allows a vehicle operator to adjust a particular vehicle speed and maintain it from the vehicle's onboard computer without the operator depressing the accelerator or brake pedal. The next generation Adaptive Cruise Control (ACC) is a computer-automated driving function that controls the vehicle speed and simultaneously manages the front and rear distances between the host vehicle and the preceding / following vehicles. Another type of automated driving function is the Collision Avoidance System (CAS), which recognizes imminent collision states and alerts the driver while simultaneously taking independently preventive measures, e.g. by steering or braking without driver interaction. Smart parking assist (IPAS) systems, lane monitoring and automatic steering ("auto steering") systems, and other advanced driver assistance (ADAS) systems, as well as autonomous driving functions, are also available in many modern automobiles.As vehicle processing, communication, and sensing capabilities continue to improve, manufacturers will continue to offer more system automated driving functions in an effort to ultimately offer fully autonomous vehicles capable of functioning between heterogeneous vehicle types in both urban and rural scenarios. Original Equipment Manufacturers (OEM) move towards vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) "speaking" cars with higher level driving automation that employ autonomous systems to enable vehicle guidance with steering, lane change, scenario planning, etc. Automated route generation systems use vehicle state and dynamics sensors, map and road state data, and path prediction algorithms to enable path generation with automated lane center prediction and lane change prediction. Computer-aided redirection techniques provide predicted alternative travel routes that may be updated based on, for example, real-time and virtual vehicle data.Variations in weather conditions, differences in surface materials, and changes in traffic loads all affect the coefficient of friction between the vehicle tires and the roadway. For example, dry and warm road conditions provide a relatively high coefficient of friction, while snow, water or ice covered road conditions provide lower coefficients of friction. Many commercially available full speed range adaptive cruise control (FSRACC) systems are designed for dry road operation only; drivers are generally recommended not to employ these systems under slippery driving conditions. However, such limitations greatly limit the functionality and range of operations of many automated driver assistance systems. Moreover, the detection of water, snow or ice on a roadway is typically subsequently determined by a host vehicle, i.e., by a sensing operation that detects low friction conditions only after the vehicle is operated under such conditions. This prevents the vehicle from taking adjustments to prospectively adapt vehicle operation to low friction road conditions.DE 10 2017 117 579 A1 describes a method for automatically detecting and safely crossing an accumulation of ice on an imminent bridge. The method automatically identifies by the vehicle the imminent approach of the vehicle to a bridge and senses an accumulation of ice on the bridge. The method then calculates a speed of the vehicle required to prevent longitudinal slip between the vehicle and the bridge and automatically decelerates the vehicle at a rate sufficient to allow the vehicle to reach the calculated speed until the time it reaches the bridge. A corresponding system is also described.US 2016 / 0 133 131 A1 describes methods and systems for the participating detection of road friction conditions by vehicles, the collection of the friction data from a large number of vehicles by a central server, the processing of the data for classifying the friction conditions according to roadway and location, and the sending of notifications about the friction conditions to the vehicles. A large number of vehicles use predictive sensing systems to determine estimates of road friction reported to the central server, where the vehicles use sensor data and dynamic vehicle conditions to estimate friction. The central server stores and aggregates the friction data, filters it, and ages it. Vehicles requesting recommendations from the central server receive indications of road friction conditions that may be important depending on their location and direction of travel. The drivers may be warned of low friction conditions, and automated vehicle systems may also respond to the messages.DESCRIPTION OF THE INVENTIONThe invention is defined by the claims.Disclosed herein are automated propulsion systems and associated control logic for intelligent vehicle operation at road conditions having different coefficients of friction, methods for constructing and operating such systems, and motor vehicles with improved clearance warning and control with adaptability to changing surface friction conditions. As an example, vehicle control algorithms and methods are presented that improve and extend the functionalities of CAS, ACC, and FSRACC around adaptability to various types of road surface conditions (e.g., dry, wet, snowy, icy, etc.), including scenarios with changing conditions for the road condition, using preview information for the road condition. While many of the available automated longitudinal control functions operate on the assumption that the vehicle is on a dry surface, the aforementioned methods and algorithms merge road condition preview information with captured vehicle dynamics and traffic data to extend the applicability of the longitudinal control to various road conditions and scenarios with changing surface frictions. From these fused data, the vehicle may implement state-dependent, vehicle-calibrated operating parameters (e.g., driving distance, speed constraints, acceleration and deceleration limits, driver warning, etc.) to prospectively adapt vehicle operation to real-time driving conditions.Attendant advantages for at least some of the disclosed concepts include ADAS architectures, control logic, and smart vehicles that incorporate road condition preview data into the longitudinal control design to extend CAS / ACC / FSRACC applicability to various road conditions and scenarios with changing surface frictions. Disclosed ADAS frameworks implementing road condition preview techniques help to increase passenger comfort while minimizing the risk of collision. Predictive road condition techniques help to ensure ADAS performance at the highest level while providing more consistent and reliable system operation without requiring the addition of additional dedicated sensors and hardware.Aspects of the present disclosure are directed to control algorithms and computer readable media for performing smart vehicle operations under road conditions having different coefficients of friction. In one example, a method of controlling an automated driving operation of a motor vehicle traversing adjacent road segments is presented. The above representative method includes, in any order and in any combination with any of the options and functions listed above and below: receiving, by the vehicle controller, sensor signals indicative of the road surface condition of the road segments from one or more local or remote sensor devices via a local or remote vehicle controller; determining, by the vehicle controller, a current (first) road friction value and a predicted (second) road friction value of a current (first) and an upcoming (second) road segment, respectively; determining whether the first road friction value is different than the second road friction value; if so, responsive to determining whether the current road friction value is greater than or less than the predicted road friction value; executing a first vehicle control action with a propulsion and / or braking system of the motor vehicle via the vehicle controller in response to the current road friction value being greater than the predicted road friction value; and executing a second vehicle control action with the propulsion and / or braking system via the vehicle controller different from the first vehicle control action in response to the current road friction value being less than the predicted road friction value.Other aspects of the present disclosure relate to motor vehicles with improved clearance warning and control with adaptability to changing surface friction conditions. As used herein, the term "motor vehicle" may include any relevant vehicle platform, such as passenger vehicles (internal combustion engine, ICE), hybrid, fully electric, fuel cell, fully or partially autonomous, etc.), commercial vehicles, industrial vehicles, tracked vehicles, off-road and all-terrain vehicles (ATV), motor cycles, aircraft, etc. Moreover, the terms "assisted" and "automated" and "autonomous" may be used interchangeably with respect to any relevant vehicle that may be classified by the Society of Automotive Engineers (SAE) level 2 to 5. For example, SAE level 0 is generally referred to as "unsupported" driving that enables vehicle-generated temporary intervention warnings, but otherwise relies solely on human control. In comparison, SAE level 3 enables unsupported, partially-assisted, and fully-assisted driving with sufficient automation for full vehicle control (steering, speed, acceleration / deceleration, etc.) while the driver must intervene within a calibrated time frame. At the top of the spectrum is level 5 automation, which completely eliminates human intervention (e.g., no steering wheel, accelerator pedal or brake pedal or shift lever).In one example, a motor vehicle is presented that includes a vehicle body having a vehicle powertrain (e.g., engine and / or engine, transmission, final drive, power train control module (PCM), etc.), a vehicle braking system (e.g., disc / drum brakes, hydraulic, brake system control module (BSCM), etc.), and a network of vehicle-side sensor devices (e.g., radar, LIDAR, infrared, camera, GPS, automated system control module (ASCM), etc.), all mounted to the vehicle body. A local vehicle controller, which may be embodied as a network of controllers, is communicatively connected to the vehicle powertrain, the brake system, and various sensor devices. The local vehicle controller is programmed to accept, collect, filter, and / or store sensor signals indicative of the road surface condition of adjacent road segments traversed by the motor vehicle from one or more sensor devices (collectively "receive"). The vehicle controller analyzes, computes, retrieves, estimates, and / or obtains (collectively "determines") road friction values for current and future road sections based on the received sensor signals.Continuing with the preceding example, the programmable vehicle controller determines whether there is a mathematical difference between a current (first) road friction value of the current (first) road segment and a predicted (second) road friction value of the incoming (second) road segment. If so, control then determines whether the first road friction value is greater than or less than the second road friction value. Responsive to the current road friction value being greater than the predicted road friction value (e.g., the motor vehicle makes a high friction (high Mu) to low friction (low Mu) transition), the controller performs a first vehicle control action with the vehicle powertrain and / or the brake system, for example, to decelerate the vehicle to a desired vehicle speed until the vehicle reaches the road friction transition point. Conversely, if the first road friction value is less than the second road friction value (e.g., if the motor vehicle makes a low-mu to high-mu transition), the vehicle controller automatically executes a second vehicle control action different from the first, e.g., with the vehicle drive and / or the brake system, to increase vehicle speed.For each of the disclosed vehicles, control systems, and methods, execution of a vehicle control action may include: determining a desired vehicle speed at a transition point where the road surface condition changes from the first to the second road friction value; determining a desired braking distance for the motor vehicle to achieve the desired vehicle speed at the transition point; and transmitting a control signal having a desired acceleration to the vehicle powertrain and / or the brake system to achieve the desired vehicle speed within the desired braking distance at the transition point. Optionally, the desired acceleration may be calculated via minimizing a cost function from speed and distance errors, the cost function being based on the desired vehicle speed and the desired vehicle distance.For each of the disclosed vehicles, control systems, and methods, the vehicle controller may determine when the motor vehicle has reached the desired braking distance to the transition point. In this case, execution of a vehicle control action may further be responsive to an affirmative determination that the motor vehicle has reached the desired braking distance. As another option, the vehicle controller may determine whether the desired acceleration is a negative acceleration (i.e., deceleration) that is greater than a maximum allowable deceleration value. If so, the controller may automatically respond by sending a warning message to a driver of the motor vehicle, e.g., via an electronic instrument cluster or telematics display unit, indicating that the deceleration value is unacceptably high, and, optionally, the driver should take appropriate action.For each of the disclosed vehicles, control systems, and methods, after executing the first vehicle control action and simultaneously with reaching the transition point, the vehicle controller may initiate a first ADAS control protocol calibrated to a predicted low road friction value. Similarly, upon execution of the second vehicle control action and simultaneously with reaching the transition point, the vehicle controller may initiate a second ADAS control protocol that is different from the first ADAS control protocol and calibrated to a predicted high road friction value. In this context, the vehicle controller may set a corresponding switching time for initiating the first / second ADAS control protocol; this switching time may be equal to or different from an actual time at which the vehicle reaches the transition point. Each ADAS control protocol may include a respective operating range for vehicle speed, a respective maximum vehicle acceleration, a corresponding maximum vehicle deceleration, and other state-dependent vehicle operating parameters. If no friction transition is detected, i.e., the difference in surface friction is zero or less than the calibrated minimum difference, the vehicle controller may initiate or otherwise maintain a first ADAS control protocol calibrated to the current road friction value.For each of the disclosed vehicles, control systems, and methods, the desired vehicle speed (e.g., in a non-target vehicle ahead scenario) may be calculated as a minimum from an operator-specified vehicle speed, a legal speed limit plus a speed tolerance, and a calibrated maximum speed for the predicted (second) road friction value of the incoming road section. As another option, the vehicle controller may: determine whether the second road friction value is below a predetermined minimum road surface friction coefficient in response to the first road friction value being greater than the second road friction value; and if so, send a warning to the operator indicating a very low surface friction condition. Executing a vehicle control action may include: determining a current path distance between a current location of the motor vehicle and a target vehicle in front of the motor vehicle; determining a transition time for the motor vehicle to reach the transition point; determining a transition path distance between the target vehicle and the motor vehicle upon reaching the transition point; and determining a desired vehicle speed at the transition point based on the current path distance, the transition time, and the transition path distance.The above summary is not intended to represent every embodiment or aspect of the present disclosure. Rather, the foregoing summary is merely an illustration of some of the novel concepts and features presented herein. The foregoing features and advantages, as well as other features and attendant advantages of this disclosure, will be apparent from the following detailed description of the illustrated examples and representative embodiments for practicing the present disclosure when taken in conjunction with the accompanying drawings and the appended claims. Moreover, this disclosure expressly includes all combinations and sub-combinations of the elements and features set forth above and below.Brief Description of the DrawingsFIG. 1 is a schematic illustration of a representative motor vehicle having a network of in-vehicle controllers, sensor devices, and communication devices for performing automated and / or autonomous driving operations, in accordance with aspects of the present disclosure. FIG. 2 is a schematic diagram illustrating a representative FSRACC system architecture of a motor vehicle in accordance with aspects of the present disclosure. FIG. 3 is a flow diagram illustrating a representative improved FSRACC ranging warning and control protocol with adaptability to changing surface friction conditions, which may correspond to stored instructions executed by onboard or remote control logic, programmable electronic control unit, or other computer-based device or device network, in accordance with aspects of the disclosed concepts.The present disclosure is susceptible to various modifications and alternative forms, and some representative embodiments are shown by way of example in the drawings and will be described in detail herein. It should be understood, however, that the novel aspects of this disclosure are not limited to the particular forms illustrated in the above-listed drawings. Rather, the disclosure is intended to include all modifications, equivalents, combinations, sub-combinations, permutations, groupings, and alternatives falling within the scope of this disclosure as encompassed by the appended claims.Detailed DescriptionThis disclosure is susceptible to embodiments in many different forms. Representative embodiments of the disclosure are illustrated in the drawings and will be described in detail herein, with the understanding that these embodiments are provided as examples of the disclosed principles and not as limitations on the broad aspects of the disclosure. As such, elements and limitations described, for example, in the summary, introduction, description, and detailed description sections, but not expressly recited in the claims, should not be individually or jointly incorporated into the claims by introduction, inference, or otherwise.For purposes of the present detailed description, unless expressly excluded: the singular includes the plural and vice versa; the words "and" and "or" are intended to be both conjunctival and disjunctive; the words "any" and "all" are intended to both mean "all"; and the words "include", "include", "having", "comprising", and the like are intended to mean "including without limitation", respectively. Moreover, words of manufacture such as "about," "fast," "substantially," "about," "generally," and the like may be used herein, for example, in the sense of "at, near, or near," or "within 0-5% of," or "within acceptable manufacturing tolerances," or any logical combination thereof. Finally, directional adjectives and adverbes, such as for example, front, rear, inside, outside, starboard, port, vertical, horizontal, up, down, front, rear, left, right, etc., may be relative to a motor vehicle, such as for example, a forward direction of travel of a motor vehicle when the vehicle is operatively aligned on a normal travel surface.Referring now to the drawings, wherein like reference numerals refer to like features throughout the several views, there is shown in FIG. 1 a representative automobile, generally designated 10, and shown herein as a sedan-style passenger car for discussion purposes. On a vehicle body 12 of the automobile 10, e.g., distributed among the various vehicle compartments, is an on-board electrical system of electronic devices for performing one or more assisted or automated driving operations. The depicted automobile 10-hereinafter also referred to as "motor vehicle" or "vehicle" for short-is only one exemplary application by which aspects and features of this disclosure may be practiced. Likewise, implementation of the present concepts for the specific FSRACC architectures and functions discussed below is also to be understood as exemplary application of the innovations disclosed herein. Thus, it will be understood that aspects and features of this disclosure are applied to other ADAS systems, used for other autonomous driving operations, and may be implemented for any logically relevant type of motor vehicle. Moreover, only selected components of the FSRACC system and vehicle are illustrated and described in more detail herein. However, the vehicles and system architectures discussed herein may include numerous additional and alternative features and other available peripheral components, for example, for performing the various methods and functions of this disclosure. Finally, the figures included herein are not necessarily to scale and are provided for teaching purposes only. The specific and relative dimensions indicated in the figures are therefore not to be understood as limiting.The representative vehicle 10 of FIG. 1 is originally equipped with a vehicle telecommunications and information unit ("telematics unit") 14 that wirelessly communicates (e.g., via cell towers, base stations, mobile switching centers (MSCs), etc.) with a remote or off-board cloud computer system 24. Some of the other vehicle hardware components 16 generally illustrated in FIG. 1 include, as non-limiting examples, a video electronic display device 18, a microphone 28, one or more audio speakers 30, and various input controls 32 (e.g., buttons, buttons, switches, touchpads, keyboards, touch screens, etc.). Generally, these hardware components 16 function, in part, as a human machine interface (HMI) to allow a user to communicate with telematics unit 14 and other systems and system components within vehicle 10. The microphone 28 provides a vehicle occupant with means for inputting verbal or other acoustic commands; the vehicle 10 may be equipped with an integrated speech processing unit utilizing human-machine (HMI) technology. Conversely, speaker 30 provides acoustic output to a vehicle occupant and may be either a stand-alone speaker intended for use with telematics unit 14 or part of audio system 22. Audio system 22 is operatively connected to a network connection interface 34 and audio bus 20 to receive analog information and play it back as sound via one or more speaker components.Communicatively coupled to telematics unit 14 is a network connection interface 34, examples of which include twisted pair / fiber Ethernet switch, internal / external parallel / serial communication bus, local area network (LAN) interface, controller area network (CAN), media oriented system transport (MOST), local interconnection network (LIN), and the like. Other suitable communication interfaces may be those conforming to the standards and specifications of ISO, SAE and IEEE. The network connection interface 34 allows the vehicle hardware 16 to send and receive signals among each other and with various systems and subsystems both inside, or "resident", to the vehicle body 12 and outside, or "remote", from the vehicle body 12. This allows the vehicle 10 to perform various vehicle functions, such as vehicle steering control, vehicle transmission control, engine fueling control, brake system turn on and off, and other automated driving functions. For example, telematics unit 14 receives and / or sends data from / to an electronic control unit (ECU) 52, an electronic control module (ECM) 54, a powertrain control module (PCM) 56, sensor interface module(s) 58, a brake system control module (BSCM) 60, and various other vehicle controllers such as a transmission control module (TCM), a climate control module (CCM), etc.With continued reference to FIG. 1, telematics unit 14 is an onboard computing device that provides a mix of services, both singly and through its communication with other networked devices. This telematics unit 14 generally consists of one or more processors 40, each of which may be embodied as a discrete microprocessor, an application specific integrated circuit (ASIC), or a dedicated control module. The vehicle 10 may provide centralized vehicle control via a central processing unit (CPU) 36, which is operatively coupled to one or more electronic storage devices 38, each of which may take the form of a CD-ROM, magnetic disk, integrated circuit (IC) device, semiconductor memory (e.g., various types of random access memory (RAM) or read only memory (ROM)), etc., and a real time clock (RTC) 42. Long-range vehicle communication capabilities with remote off-board networked devices may be provided via one or more or all of a / r cellular chipset / component, a / r navigation and location chipset / component (e.g., global positioning system (GPS) receiver), or a wireless modem, all of which are collectively shown at 44. Short-range wireless connectivity may be provided via a short-range wireless communication device 46 (e.g., a Bluetooth® unit or near field communication transmitter / receiver (NFC)), a dedicated short-range communication (DSRC) component 48, and / or a dual antenna 50. It should be understood that the vehicle 10 may be implemented without one or more of the above-mentioned components or may include additional components and functions desired for a particular end use. The various communication devices described above may be configured to communicate data as part of a periodic transmission in a vehicle-to-vehicle (V2V) communication system or a vehicle-to-everything (X), V2X) communication system, such as a vehicle-to-infrastructure (V2I), a vehicle-to-pedestrian (V2P), and / or a vehicle-to-device (V2D) communication system, The exchange.The CPU 36 receives sensor data from one or more sensor devices that use, for example, photodetection, radar, laser, ultrasound, optical, infrared, or other suitable technology for performing automated driving, including short-range communication technologies such as DSRC or Ultra-Wide Band (UWB). According to the illustrated example, the automobile 10 may be equipped with one or more digital cameras 62, one or more range sensors 64, one or more vehicle speed sensors 66, one or more vehicle dynamics sensors 68, and any required filtering, classifying, fusion, and analysis hardware and software for processing raw sensor data. The digital camera 62 may use a charge coupled device (CCD) sensor or other suitable optical sensor to generate images that indicate a field of view of the vehicle 10, which may be configured for continuous imaging, e.g., at least about 35 images generated per second. By way of comparison, the range sensor 64 may emit and detect reflected radio, electromagnetic, or light-based waves (e.g., radar, EM inductive, light detection and ranging (LIDAR), etc.) to detect, for example, the presence, geometric dimensions, and / or distance of an object. The vehicle speed sensor 66 may take various forms, including wheel speed sensors that measure wheel speeds, which are then used to determine the vehicle speed in real-time. In addition, the vehicle dynamics sensor 68 may be in the form of a single- or triaxial acceleration sensor, a rotation rate sensor, an inclinometer, etc., for detecting longitudinal and lateral accelerations, yaw, roll and / or inclination rates or other dynamic parameters. Using the data of the sensor devices 62, 64, 66, 68, the CPU 36 identifies the forward road surface condition, determines the characteristics of that road surface condition, identifies objects within a detectable range of the vehicle 10, determines the characteristics, such as size, relative position, approach angle, relative speed, etc., of the target object, and performs automated control maneuvers based on these performed operations.These sensors are distributed throughout the motor vehicle 10 in functionally unblocked positions relative to front-to-rear direction of view or on the port and starboard sides of the vehicle. Each sensor generates electrical signals indicative of a property or condition of a target object, generally as an estimate having a corresponding standard deviation. While the operating characteristics of these sensors generally supplement each other, some are more reliable than others in estimating certain parameters. Most sensors have different operating ranges and coverage ranges and are capable of sensing different parameters within their operating range. For example, a radar-based sensor may estimate the distance, range rate, and azimuth position of an object, but may not be robust in estimating the size of a detected object. Cameras with optical processing, on the other hand, may be more robust in estimating a shape and azimuth position of an object, but may be less efficient in estimating the distance and range rate of the object. A scanning LIDAR-based sensor may operate efficiently and accurately with respect to estimating distance and azimuth position, but may not be able to accurately determine the range rate, which may be inaccurate in detecting and recognizing new objects. Ultrasonic sensors, on the other hand, are capable of estimating the distance, but generally cannot accurately estimate the range rate and the azimuth position. Moreover, the performance of many sensor technologies may be affected by different environmental conditions. Thus, sensors generally have parametric deviations whose operative intersections offer opportunities for sensory fusion.Turning next to FIG. 2, a representative architecture of a FSRACC system 100 is shown that maintains and adjusts a set vehicle speed to enable approaching a "target" front vehicle by using predicted path information of the respective vehicle and the front vehicle, the distance between the respective vehicle and the front vehicle, and the driver's commands. In the illustrated architecture of the FSRACC system 100, a road condition detection module (RCDM) 102 collects and exchanges information with an automated system control module (ASCM) 104, which in turn communicates with an adaptive cruise control module (ACCM) 106 for performing automated driving operations of a motor vehicle 110. The ACCM receives various inputs, such as sensor data from one or more environmental sensors 108, driver inputs from one or more in-vehicle driver input devices 112, as well as any other sensor inputs, control signals, and associated data described herein. The received driver inputs may be a driver determined vehicle speed and optionally a driver determined vehicle distance in nature. For an autonomous vehicle application, the aforementioned driver inputs may be replaced or supplemented by a vehicle speed specified by the controller and / or a vehicle distance specified by the vehicle. Although differing in appearance, the vehicle 110 of FIG. 2 may take any of the options and alternatives described above with respect to the vehicle 10 of FIG. 1, and vice versa.The architecture of the FSRACC system 100 of FIG. 2 is configured to improve the functionalities of CAS, ACC, and FSRACC and to extend with adaptability to different types of road surface conditions (e.g., dry, wet, snow-covered, icy, etc.), including demanding scenarios with changing road conditions based on preview information about the road condition. For example, the RCDM 102 is operable to collect all the necessary data to generate the necessary preview information for the road condition for performing each disclosed driving operation of the vehicle. Such preview information for the road condition may be generated by a visual, image-based sensor network that identifies ice, snow, and / or precipitations that are scattered from a wheel of a host and / or target vehicle wheel coming from the surface of the road, as described, for example, in commonly owned U.S. Pat. Nos. 9,971,945 B2 and 9,972,206 B2. Preview information for the road condition may also be generated using a distributed network of onboard sensing devices to monitor forward roadway segments and derive surface conditions with recursive adaptive learning and validation, such as in commonly owned U.S. Pat. No. 9,139,204 B1. Other commonly owned disclosures relating to evaluating road surface conditions near and in front of a moving vehicle are U.S. Patent Application, US 2019 / 0 057 261 A1 filed August 15, 2017 (road condition classification with a single convolutional neural network); US 2019 / 0 057 272 A1 filed August 18, 2017 (road condition detection with optical imaging of profile prints); US 2019 / 0 188 495 A1 filed December 18, 2017 (road condition detection by panoramic image detection); and US 2020 / 0 074 639 A1 filed September 4, 2018 (road condition detection by inclusion of ambient illumination). All patents and patent applications mentioned in this paragraph are incorporated herein by reference in their respective entireties and for all purposes.During a cruise control operation, the FSRACC system 100 may have several problems including adjusting control to reduced Mu and low Mu friction conditions, as well as transitions in surface friction from low to high and from high to low. For a high to low road friction transition, the FSRACC system 100 may derive a desired vehicle speed at a surface friction transition point to help ensure harmless speed transitions while maintaining a desired front distance, if present. Achieving this desired vehicle speed at the transition point may require deriving an entry time for the braking operation at which the vehicle braking system is activated to ensure a comfortable and yet safe deceleration rate. The FSRACC system 100 may also determine an activation time to initiate a low-Mu CAS / ACC control protocol for vehicle operation on a reduced friction road surface. For very low friction surface conditions, the FSRACC system 100 may issue a corresponding driver warning and optionally disable the ACC operation. For a low-to-high road friction transition, the FSRACC system 100 may determine when to switch from a low mu CAS / ACC control protocol to a standard or high mu CAS / ACC control protocol, e.g., after reaching the surface friction transition point.With continued reference to FIG. 2, a goal of ACC / FSRACC may generally be achieved in control via minimizing a cost function of speed / displacement errors to obtain a desired acceleration command. Once a desired speed value and a desired inter-vehicle distance are determined, for example, a local or remote vehicle controller, such as the CPU 36 of FIG. 1 or the ACCM 106 of FIG. 2, may calculate a desired acceleration value via minimizing a linear-quadratic optimization cost function J of speed and distance errors to realize a desired speed (e.g., deceleration required to reduce the current vehicle speed so as to be at the transition point within the desired vehicle speed range for low mu values): where u is the desired acceleration represented as a scalar value; u T is a transformation format of u; x is a vector size; x T is a fault state function for speed and distance (e.g., a transformation format of x); R and Q are defined weighting matrices. In the above cost function, the fault condition function x T may be calculated for speed and distance as a function of: where x1 is a distance fault condition; x2 is a speed fault condition; v d is the desired speed; v is a current vehicle speed; d is a current vehicle distance to a target vehicle in front of the motor vehicle; and d d is a desired vehicle distance to the target vehicle. The weighting matrices R and Q may include weighting values to help correct or otherwise compensate for the unequal data redundancy. The parameters Q and R can be set as design parameters in order to suppress state variables and control signals. Weighting factors within each matrix (e.g., Q=[q 1 q 2 / q 3 q 4] and R=r) may be tuned to achieve natural driving. The control input u may be configured to minimize the cost function J to achieve a minimum speed and following distance error. It is contemplated that other mathematical optimization techniques may be used to perform the disclosed concepts; the control input u may take on different equation formats, for example, depending on which specific method is used to minimize the cost function.The RCDM 102 generates sensor signal data indicative of the road surface conditions of adjacent road segments over which the host vehicle 110 is currently navigated and predicted to pass. This data, which is represented in FIG. 2 about an existing (first) friction coefficient μ C of a current (first) road surface segment and a predicted (second) friction coefficient μ P of a future (second) road surface segment, is transmitted to the ASCM 104 for aggregation, filtering, analysis, and storage. A friction condition evaluation log 101 determines from the present and predicted friction values μ C and μ P, which is estimated from a series of road friction scenarios that it occurs, such that the FSRACC system 100 may recommend that the ACCM 106 perform a corresponding control action. These road friction scenarios may include, as some non-limiting examples: a high-to-low (high risk) surface friction scenario, a high-to-reduced (medium risk) surface friction scenario, a reduced-to-low (low risk) surface friction scenario, a low-to-reduced surface friction scenario, a reduced-to-high surface friction scenario, a low-to-high (low risk) surface friction scenario, and a friction scenario without friction variation.Upon completion of the determination of the road friction scenario, the ASCM 104 executes a friction change preview protocol 103 to determine whether or not a friction change has been predicted. As a non-limiting example, the road condition preview information provided by the RCDM 102 may be analyzed via the ASCM 104 to characterize each road surface segment as one of a plurality of standardized discrete conditions, such as a dry condition, a wet condition, a water-filled condition, an ice-covered condition, an ice-covered condition, a snow-covered condition, a snow-covered condition, etc. Each discrete condition may be associated with a predetermined coefficient of friction that may be represented as a respective mu rating μ n. The predicted friction μ P from the road condition detection module 102 may be discrete, while the current friction μ C, if provided by other methods, may be continuous; the comparison error between μ P and μ C may be clipped to result in a discrete condition. If no friction change is predicted, a current CAS / ACC control protocol 105 may be maintained; this determination is communicated to the vehicle 110 via ACCM 106. Conversely, if a friction change of sufficient magnitude is predicted, a friction transition warning and control protocol 107 may be invoked and executed, an example of which is discussed below with reference to FIG. 3.Referring to the flowchart of FIG. 3, an improved method or control strategy for controlling a control automated driving operation of a motor vehicle, such as an ADAS maneuver of the vehicle 10 of FIG. 1 or a FSRACC maneuver of the vehicle 110 of FIG. 2, is generally described at 200 in accordance with aspects of the present disclosure. Some or all of the operations illustrated in FIG. 3 and described in more detail below may be representative of an algorithm corresponding to processor-executable instructions stored, for example, in main or auxiliary or remote memory and executable, for example, by a local or remote controller, processing unit, control logic circuit, or other module, device, and / or network of devices to perform any or all of the functions associated with the disclosed concepts described above or below. It should be appreciated that the order of execution of the illustrated method blocks may be changed, additional blocks added, and some of the described blocks may be changed, combined, or eliminated.The method 200 begins at the header with processor executable instructions for a programmable controller or control module or similar suitable processor to invoke a mu-based adaptation protocol initialization method to control operation of an automated driving system that controls inter-vehicle distance during an ADAS maneuver. This routine may be executed in real-time, continuously, systematically, sporadic, and / or at periodic intervals, e.g., every 100 milliseconds, etc., during ongoing vehicle operation. As yet another option, process block 201 may be initialized in response to a user input or a broadcast request signal from a backend or middleware computing node that is in charge of collecting, analyzing, sorting, storing, and distributing vehicle data. During the initialization procedure at block 201, the local vehicle telematics unit 14 may execute a navigation processing code segment, e.g., to obtain geospatial data, vehicle dynamics data, time stamps and associated time data, etc., and optionally display selected aspects of this data to an occupant of the vehicle 10.Continuing with the discussion of method 200, instructions stored at decision block 203, e.g., via FSRACC system 100, are executed to determine whether or not a friction change has been predicted, as described above in the discussion of FIG. 2. If a negative determination is returned (block 203=N), a currently active CAS / ACC control protocol may be maintained or a CAS / ACC control protocol calibrated to the current road surface friction may be activated at method block 205. At node, method 200 may proceed to and end at method block 207, or may loop back to method block 201 and run in a continuous loop. Upon determining that a surface friction change is expected (block 203=J), the method 200 proceeds to the method block 209 and initializes an internal ECU hardware clock (sets time T=0) and a geolocation service (sets actual vehicle position S(T=0)=0). This initialization procedure can be done within software code by variable setup to keep track of when certain loop computations are to be started and ended, respectively.Once the ADAS system confirms that a change in road friction is expected and, in response, initializes all underlying buffer and backend processes, the method 200 proceeds to decision block 211 to determine whether the current road friction value of the current road segment is greater than or less than the predicted road friction value of the incoming road segment. If it is determined that the current road friction value is less than the predicted road friction value, i.e., a transition between low and high surface mu is expected (block 211=N), the method 200 proceeds to method block 213 to determine an (first) ADAS shift time suitable for initializing a (first) ADAS control protocol calibrated to the predicted high road friction value. After deriving an appropriate shift time, the method 200 proceeds to method block 215 to determine the distance traveled of the motor vehicle (new vehicle position S(T)=S(T-1)+V·ΔT)) since the internal clock of the ECU hardware was initialized at method block 209. Upon moving to decision block 217, a suitable vehicle controller or dedicated control module estimates whether or not the host vehicle has reached a switching point (e.g., the switching time has elapsed or the host vehicle has reached the switching point). If not (block 217=N), a time value is returned and incremented (T++) at process block 219 and the process returns to decision block 211. Conversely, if the vehicle has reached a temporal and / or spatial switching point (block 217=J), the vehicle controller in response initiates the high mu ADAS control protocol at method block 221; the method then continues to method block 207 and ends or resets.Referring to decision block 211 of FIG. 3, in determining that the current road friction value of the road segment being traveled by the vehicle is greater than the predicted road friction value of a future road segment, i.e., a high-to-low surface mu transition is expected (block 211=J), method 200 provides stored executable instructions to predefined method block 223 for the ADAS system (e.g., via FSRACC system 100 of FIG. 2 ) to: (1) determine a desired vehicle speed at the mu transition point; (2) determine a desired braking distance to achieve the desired vehicle speed at the mu transition point; (3) determine an appropriate (second) transition time to initiate a (second) ADAS control protocol calibrated to the predicted low road friction value; and (4) determine a warning distance for a driver to take action (e.g., brake) when the desired amount of deceleration is greater than an allowable maximum amount of deceleration due to a very low predicted surface friction. Each of the aforementioned control measures will be explained in more detail below.Upon completion of the predefined operations listed above with respect to method block 223, method 200 proceeds to method block 225 to determine the traveled distance of the motor vehicle (new vehicle position S(T)=S(T-1)+V-ΔT)) since the internal clock of the ECU hardware was initialized at method block 209. A suitable vehicle controller or dedicated control module, upon passing to decision block 227, determines whether or not the host vehicle has reached the desired braking distance. If so (block 227=J), the method 200 proceeds to the method block 229 and outputs one or more corresponding control signals to the vehicle powertrain and / or the brake system to achieve the desired acceleration / deceleration and thus achieve the desired vehicle speed within the desired braking distance at the transition point. In response to a determination that the braking distance from the host vehicle has not been reached (block 227=N), the method 200 determines whether or not the host vehicle has reached a low-mu insufficient braking distance at decision block 231. If so (block 231=J), a low friction warning message is sent to the driver at input / output block 233. This message may include a request to the driver to take action immediately in light of the expected decrease in road surface friction. Alternatively, the vehicle 10, 110 may perform an automated driving maneuver to improve the expected friction drop.Prior to or concurrently with determining that the host vehicle has reached an insufficient braking distance threshold in light of impending low-mu road conditions (block 231=N), an appropriate vehicle controller or special control module determines whether or not the host vehicle has reached a switching point (e.g., the switching time has elapsed or the host vehicle has reached the transition point) at decision block 235. If not (block 235=N), a time value is returned and incremented (T++) at process block 219 and the process returns to decision block 211. However, if the vehicle has reached a temporal and / or spatial switching point (block 235=J), the vehicle controller in response initiates the low mu ADAS control protocol at method block 237; the method continues to method block 207 and ends or resets.When low road surface friction is predicted, a host vehicle currently traversing a road segment with high surface friction and sufficient distance should be able to reduce the vehicle speed by a comfortable braking level to a desired vehicle speed at the transition point to ensure safe and comfortable driving on the low μ surface. If it is determined that the vehicle needs to brake with more than a maximum allowable deceleration on the high μ surface to achieve the desired vehicle speed at the transition point, a warning message may be generated to warn the driver to take action; the vehicle controller may automatically deactivate ACC / FSRACC. Once the vehicle is sufficiently close to the transition point, the low μ ACC / FSRACC configuration is timely enabled for a smooth transition.Calculating the desired vehicle speed v d at the friction transition point may include many approaches. For a high-to-low surface friction transition without a target vehicle in front of the host vehicle, the current vehicle speed V of the subject vehicle at the transition point may be adjusted to a reduced vehicle speed v d to be no more than a maximum allowable speed on the low μ surface as calculated according to: where V d( up) is the desired vehicle speed; V set is an operator-set vehicle speed; V spd_limit is a legal speed limit of the incoming road section; Δ1 is a vehicle-calibrated speed tolerance (e.g., + / - 5 mph); and V maxspd(µp) is a maximum speed vehicle-calibrated to the road surface state of the incoming road section.For a high-to-low surface friction transition with a detected target vehicle in front of the host vehicle, the vehicle controller will: monitor a current speed V t of the target vehicle and a current speed V h of the host vehicle; calculate whether the speed V t of the target vehicle is less than the speed V h of the host vehicle; and determine a transition distance D between the host vehicle and the transition point, and a distance distance R between the host vehicle and the target vehicle. From these determinations, the vehicle controller may quantify an estimated time Δt 1 required for the subject vehicle to reach the transition point and a desired vehicle speed V d at the transition point to ensure that the host vehicle does not collide with the target vehicle. The following time-to-collision inverse TTC -1 may be used to determine which control strategy to use in a particular scenario:If TTC -1 >0, the vehicle controller initiates a surface μ transition control strategy calibrated to scenarios where a target vehicle is not detected. On the other hand, if it is determined that TTC -1< 0, a desired vehicle speed is determined for the transition point in the case where a target vehicle has been determined. To ensure a safe distance distance at the transition point, a minimum allowable distance between the two vehicles on a low μ surface may be determined as: where L min( μ p) + T s( μ p) V t represents a constant following distance, and represents a transition speed adjustment distance with a maximum allowable delay. The actual distance between the host and target vehicles when the host vehicle reaches the transition point may be set to be greater than or equal to a minimum allowable distance d safeEquation (3) may be obtained based on equations (1) and (2), where f is a function of the current velocity V h of the host vehicle, the distance R, the distance rate A=R, and the transition distance D. In this case, the desired speed of the host vehicle may be determined as: where Δ is a small speed adjustment value.For a high-to-low road surface friction transition where the braking distance may not be sufficient to achieve a desired vehicle speed at the transition point, it may be advantageous for the vehicle controller to determine whether and when to send a warning to a vehicle occupant to take corrective actions, and, optionally, automate if and when remedial actions such as temporarily disabling ACC / FSRACC operation. Once a desired host vehicle speed is determined for the transition point, the vehicle controller may determine a braking distance to achieve the desired vehicle speed and a warning distance to the driver to take actions to reduce the speed to a suitable level at the transition point. The brake distance d brake for comfortable braking may be calculated as follows: where α com( μ c) is a calibrated comfortable deceleration amount for predicted surface friction μ c ; τ sys_delay is a total system time deceleration; V h is the respective vehicle speed; and L safe is a calibrated safe distance. A warning distance d warning for a driver to brake manually due to a high-to-low friction transition may be calculated as follows: where τ drv_delay is a deceleration by the driver; α max( μ c) is a maximum deceleration calibrated to a current road friction μ c ; L safe( μ c) is a minimum vehicle calibrated distance based on the current road friction μ c; and τ sys_delay is a system deceleration.In at least some applications, a maximum acceleration / deceleration value may be derived from a surface friction dependent maximum longitudinal force. For a given vehicle maneuver, this longitudinal force may be retrieved from a stored, vehicle calibrated look-up table derived from plots of slip rate (λ) versus tire longitudinal force (F T_x) from vehicle tests. The longitudinal dynamics of the vehicle may be mathematically quantified as: wherein the longitudinal slip ratio at each wheel at acceleration is calculated as: and wherein ρ is a current air density; C d is a current drag coefficient; A is a front surface; θ is a gradient angle of the roadway; α is the acceleration; V x, V y are the vehicle longitudinal and lateral velocities, respectively; r is a yaw rate of the vehicle; m is a vehicle mass; w is a wheel speed; R is an effective tire radius; and F x= F xf+ F xr is a sum of front and rear axle longitudinal forces.The high-to-low friction transition surface friction-based transition warning and control may further include determining an appropriate time frame for when to enable an appropriate ACC / FSRACC control protocol calibrated to the predicted road friction. For such a transition, the switching time for activating the predicted friction-based ACC / FSCC configuration may include a buffer time value to compensate for system delays and provide timely conversion to the low friction-based control strategy. A switching point and the size of a transition zone adjacent to the transition point may be calculated as follows: where d tzone is the size of the transition zone; ΔL is a tolerance length of a high-to-low transition; and τ sys_delay is an offset value for the system delay. If a vehicle distance to the transition point D is equal to the transition zone d tzone the vehicle controller will need to switch to an ACC / FSCC configuration based on the predicted low friction. Conversely, for a low-to-high transition of road friction, the predicted high friction ACC / FSCC configuration may be scheduled with a delay to ensure that the host vehicle has completely left the low friction road segment. In this case: wherein L veh is a vehicle length; and ΔL is a tolerance length for a low-to-high transition. If the host vehicle travels beyond the override point, the vehicle controller automatically switches to a high friction based control strategy. It should be understood that many of the above equations and formulas, such as the proposed equations for calculating V d, d brake, d warning, d tzone, etc., are proposed as exemplary ways to implement aspects of the disclosed concepts. Therefore, these features may be determined using other suitable approaches so long as this approach follows the philosophy herein for control for changing surface conditions.As mentioned above, aspects of the disclosed concepts cover systems, methods, and vehicle control algorithms that improve and extend operation of longitudinal distance controls such as CAS / ACC / FSRACC to scenarios with changing road conditions. Aspects of the disclosed concepts also cover methods for determining desired host vehicle speeds at road surface transition points with and without detected front obstacles, such as target vehicles, pedestrians, animals, etc. Further aspects of the disclosed concepts cover methods for determining a braking distance suitable for reaching the transition point at a desired speed. Further aspects of the disclosed concepts include methods to determine whether and when to issue a driver warning for an impending low road friction scenario and methods to determine when to switch to an ACC / FSCC configuration based on a new road friction after the transition.Aspects of this disclosure may be implemented, in some embodiments, by a computer-executable program of instructions, such as program modules, commonly referred to as software applications or application programs, executed by any of a controller, or the controller variants described herein. Software may include, in non-limiting examples, routines, programs, objects, components, and data structures that perform particular tasks or implement particular types of data. The software may interface to allow a computer to respond according to an input source. The software may also cooperate with other code segments to initiate a plurality of tasks in response to data received in connection with the source of the received data. The software may be stored on any of a variety of storage media, such as CD-ROMs, magnetic disks, magnetic bubble memories, and semiconductor memories (e.g., various types of RAM or ROM).Moreover, aspects of the present disclosure may be embodied with a variety of computer system and computer network configurations, including multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. Moreover, aspects of the present disclosure may be practiced in distributed computing environments where tasks are performed by local and remote processing devices connected via a communication network. In a distributed computing environment, the program modules may reside on both local and remote computer storage media including storage devices. Aspects of the present disclosure may therefore be implemented in conjunction with various hardware, software, or a combination thereof, in a computer system or other processing system.Each of the methods described herein may include machine readable instructions intended for execution by: (a) a processor, (b) a controller, and / or (c) any other suitable processing device. Any algorithm, software, control logic, protocol, or method disclosed herein may be embodied as software stored on a tangible medium, such as flash memory, a CD-ROM, a floppy disk, a hard disk, a digital versatile disk (DVD), or other storage devices. The entire algorithm, control logic, protocol, or method, and / or portions thereof may alternatively be executed by a device other than a controller and / or executed in firmware or dedicated hardware in an available manner (e.g., implemented by an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable logic device (FPLD), discrete logic, etc.). Additionally, although specific algorithms are described with reference to the flowcharts illustrated herein, many other methods for implementing the example machine readable instructions may alternatively be used.

Claims

A method for controlling automated driving of a motor vehicle crossing first and second road segments, the method comprising: receiving sensor signals indicative of road surface conditions of the first and second road sections from a sensor device via a vehicle controller of the motor vehicle; determining first and second road friction values of the first and second road sections, respectively, via the vehicle controller based on the received sensor signals; determining whether the first road friction value is different from the second road friction value; responsive to the first road friction value being different from the second road friction value, determining whether the first road friction value is greater or less than the second road friction value; executing a first vehicle control action with a propulsion and / or braking system of the motor vehicle via the vehicle controller in response to the first road friction value being greater than the second road friction value; and executing a second vehicle control action with a propulsion and / or braking system of the motor vehicle via the vehicle controller in response to the first road friction value being less than the second road friction value; wherein executing the first vehicle control action includes: determining a desired vehicle speed at a transition point where the road surface conditions change from the first road friction value to the second road friction value; determining a desired braking distance for the motor vehicle to achieve the desired vehicle speed at the transition point; transmitting a control signal having a desired acceleration to a vehicle powertrain and / or a vehicle braking system to achieve the desired vehicle speed within the desired braking distance at the transition point; the method further comprising: determining whether the desired acceleration is a deceleration value greater than a maximum allowable deceleration value; and transmitting a warning message to a driver of the motor vehicle indicating that the deceleration value is unacceptably high in response to the deceleration value being greater than the maximum allowable deceleration value.The method of claim 1, further comprising computing the desired acceleration by minimizing a cost function of velocity and distance errors J as: J = following following following ≅ ( x T Q x + u T R u ) d t where u is the desired acceleration; u T is a transformation format of the desired acceleration u; x T is an error condition function for the velocity and distance; x is a vector size; R and Q are defined weighting matrices.The method of claim 2, wherein the fault condition function for the speed and distance x T is determined as a function of: x T = [ x 1 x 2 ] = [ d d - d v d - v ] where x1 is a distance fault condition; x2 is a speed fault condition; v d is the desired speed; v is a current vehicle speed; d is a current vehicle distance to a target vehicle in front of the motor vehicle; and d d is a desired vehicle distance to the target vehicle.The method of claim 1, further comprising determining whether the motor vehicle has reached the desired braking distance, wherein the execution of the first vehicle control action is additionally responsive to the motor vehicle having reached the desired braking distance.The method of claim 1, further comprising: initiating, via the vehicle controller, a first ADAS control protocol calibrated to the second road friction value after executing the first vehicle control action and approximately when the vehicle reaches the transition point; and initiating, via the vehicle controller, a second ADAS control protocol different from the first ADAS control protocol and calibrated to the second road friction value after executing the second vehicle control action and approximately when the vehicle has reached the transition point.The method of claim 5, further comprising: determining a first switching time to initiate the first ADAS control protocol; and determining a second switching time to initiate the second ADAS control protocol.The method of claim 5, wherein the first and second ADAS control protocols each include: a respective vehicle speed operating range; a respective maximum vehicle acceleration; and a respective maximum vehicle deceleration.The method of claim 1, wherein executing the first and second vehicle control actions each includes determining a respective desired vehicle speed at a transition point where the road surface conditions change from the first road friction value to the second road friction value, the desired vehicle speed being calculated as: V d ( μ p ) = min ( V s e t, V s p d l i m i t + Δ 1, V m a x s p d ( μ p ) ) wherein V d( μ p) is the desired vehicle speed; V set is an operator set vehicle speed; V spdlimit is a legal speed limit of the second road section; Δ1 is a speed tolerance; and V maxspd(µp) is a calibrated maximum speed for the road surface condition of the second road section.

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