Adaptive Braking and Direction Control System (ABADCS)

Abstract

A method for controlling and optimizing the braking and direction control of a vehicle operating on a contaminated, compliant, or non-compliant surface. The method includes the steps of: collecting data from a plurality of sensors, the data indicating the condition of the contaminated, compliant, or non-compliant surface; and sending the data to a neural controller having an algorithm configured to process the data. The algorithm includes: determining optimal braking and direction control instructions for the vehicle, generating warnings and alerts based on the calculated optimal braking and direction control instructions, and sending the optimal braking and direction control instructions to the braking and steering systems of the vehicle, and sending the warnings and alerts to an alert and warning system of the vehicle. The method further includes adjusting the steering and direction control of the braking and steering systems according to the optimal braking and direction control instructions provided by the neural controller.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 62 / 694,719, filed Jul. 6, 2018, which is incorporated herein by reference in its entirety. Field of the Invention

[0003] The present invention generally relates to vehicle braking and steering control systems and, more particularly, to systems designed to optimize the braking and steering response of a vehicle. Background of the Invention

[0004] As used hereinafter, the term "vehicle" refers to any vehicle that is stopped using wheel brakes, including but not limited to automobiles, trucks, airplanes, military vehicles, vocational vehicles, recreational vehicles, etc. Current vehicle braking and steering control systems do not have sensing or other inputs for identifying the type of contaminants on the surface (e.g., rain, snow, gravel, etc.) or the type of surface on which the vehicle is traveling. As a result, such control systems are unable to adjust or optimize their performance for different conditions.

[0005] Current anti-lock braking systems (ABS) and anti-skid braking systems (ASBS) prevent wheels from locking, skidding, decelerating too quickly, and / or decelerating to a slip rate that exceeds a certain value. The slip rate is measured relative to the ground speed of the vehicle or relative to other wheels of the vehicle.

[0006] Current anti-lock braking systems (ABS) and anti-skid braking systems (ASBS) can prevent wheels from locking, skidding, decelerating too quickly, and / or decelerating to a slip rate that exceeds a certain value. The slip rate is measured relative to the ground speed of the vehicle or relative to other wheels of the vehicle.

[0007] These systems can be linear manual, analog, mechanical, hydraulic, electrical, electronic, digital (fly-by-wire, steer-by-wire) or hybrid systems. In these systems, a person or a computer (e.g., during automatic operation) controls the steering of the vehicle based on hand, foot, electronic, or computer control. These controls rotate the wheels and / or the rudder and input a yaw or lateral rotation about the vehicle's center of gravity (CoG). The vehicle's response to the foregoing steering inputs can also be assisted by a stability control system.

[0008] One drawback of these systems is that they are optimized for operation on dry road surfaces. The operating algorithms used in these systems attempt to keep the slip ratio within a low value associated with threshold braking on a dry road surface or a hard non-compliant surface, typically in the range of 10% to 20%. Although certain tires are designed and manufactured to optimize performance on different kinds of surfaces (e.g., the siping and grooves in snow tires), current braking systems use the same control method regardless of whether the vehicle is on a dry road surface or another surface. However, the response of the vehicle will vary depending on the condition and type of the surface.

[0009] Another drawback of current systems is that, although the various control systems of a vehicle tend to operate independently, they often manipulate the same systems of the vehicle. For example, ABS vehicles and ASBS aircraft typically do not decelerate as quickly on snow-covered roads. As a result, since ABS and ASBS may cause or allow the vehicle tires to undergo viscous slippage, wheel and rudder steering may not respond quickly to the operator's control inputs.

[0010] The present application provides a system that optimizes the braking and steering capabilities of a vehicle while taking into account the condition of the surface on which the vehicle is traveling. Summary of the Invention

[0011] An adaptive braking and direction control system (ABADCS) for ground vehicles and aircraft is provided, the system comprising: a plurality of sensors and digital data connections / all available current and expected future data source inputs for describing the current operating condition of the vehicle, including but not limited to speed, optimal / adaptive road surface braking and / or steering traction, and the changing speed of the vehicle equipped with ABADCS and the resulting current time interaction with fixed infrastructure (GIS) and moving obstacles (other vehicles, etc.); an adaptive control processing system, which includes an artificial intelligence-enabled adaptive braking subsystem that, when the braking or steering required by the driver is greater than the braking or steering force provided by the tires on a contaminated or unclean, non-dry, non-high-traction road surface, participates and utilizes data from the plurality of sensors to determine the optimal braking and steering actions, where optimal is defined as the highest braking force, the highest steering force, and the safest intersection of the most likely obstacle and hazard avoidance paths; a user feedback system, including a set of tactile, auditory, and visual warnings, alerts, and feedback methods that enhance the driver's situation awareness under contaminated driving conditions.

[0012] The aforementioned vehicle can be operated in a manner that the operator is unaware of or ignores the real-time operating conditions. It is conceivable that the ABADCS can warn the operator through auditory and / or visual and / or tactile means that there are operating conditions that are not conducive to the safe operation of the vehicle. It is conceivable that the ABADCS can allow the operator to operate the vehicle in a way that prevents the ABADCS from assisting in the safe operation of the vehicle. For example, the operator may drive too fast to stop the vehicle within the required distance or safely steer near an obstacle. In the case where the ABADCS cannot assist the operator in achieving braking and / or direction control, if the operator requests a turn, the ABADCS can be configured to default to pedal rhythm braking, and if the operator does not request a turn, it defaults to simulated braking ((lack of direction control (such as understeer) or restoration of direction control (such as "fishtailing" or oversteer)).

[0013] A method for controlling and optimizing the braking and direction control of a vehicle operating on a contaminated, compliant, or non-compliant surface is provided. The method includes the steps of: collecting data from a plurality of sensors, the data indicating the condition of the contaminated, compliant, or non-compliant surface; sending the data to a neural controller having an algorithm configured to process the data, the algorithm including: determining the optimal braking and direction control instructions for the vehicle, generating warnings and alerts based on the calculated optimal braking and direction control instructions, and sending the optimal braking and direction control instructions to the braking and steering systems of the vehicle and the alerts and warnings to a warning and alert system of the vehicle. The method further includes the step of adjusting the steering and direction control of the braking and steering systems according to the optimal braking and direction control instructions provided by the neural controller.

[0014] In the aforementioned method, it can be expected that after the step of generating warnings and alerts, there may be a step of generating an arming signal to a semi-automatic or fully automatic braking system to pre-arm the braking and steering systems based on real-time situations that may exist when the vehicle needs to brake and / or perform direction control.

[0015] It can also be expected that the method can include the step of sending a command from the user to the braking and steering systems of the vehicle to increase braking or steering beyond the optimal braking and steering instructions provided by the neural controller.

[0016] In the aforementioned method, it can be expected that the user can be at least one of a person, a computer, and an automated machine computer.

[0017] It is also conceivable that the neural controller can be started before detecting a contaminated, compliant, or non-compliant surface.

[0018] It is also anticipated that the vehicle can be one of a manual, semi-automatic, and automatic vehicle.

[0019] In the foregoing method, the neural controller can be set to repeatedly monitor the condition of a contaminated, compliant, or non-compliant surface and send updated braking and direction control instructions to the braking and steering systems of the vehicle.

[0020] In the foregoing method, the neural controller can be set to repeatedly monitor the condition of a contaminated, compliant, or non-compliant surface and send updated braking and direction control instructions to the braking and steering systems of the vehicle, wherein the arithmetic pre-arming of the semi-automatic and fully automatic braking systems can be adjusted in real time according to real-time conditions.

[0021] It is also anticipated that the neural controller can be part of a network that includes multiple neural controllers in other vehicles. In such a network, the neural controller can communicate with the multiple neural controllers to relay data regarding contaminated or non-compliant surfaces.

[0022] It can further be anticipated that the neural controller can be set to use data for multiple neural controllers to adjust the braking and direction control instructions sent to the braking and steering systems of the vehicle.

[0023] In the foregoing method, the neural controller can be part of a network that includes multiple neural controllers in other vehicles. In such a network, the neural controller can communicate with the multiple neural controllers to relay alerts and warnings regarding contaminated or non-compliant surfaces.

[0024] In the foregoing method, the maximum braking force cannot exceed a level that causes unacceptable damage to the tires or other components of the vehicle.

[0025] It is further anticipated that the neural controller can be set to adjust the optimal braking and direction control instructions to reduce the risk of tire failure.

[0026] In the foregoing method, the vehicle can be an aircraft.

[0027] The present invention can also provide a device for controlling and optimizing the braking and direction control of a vehicle, which device may include: a plurality of sensors configured to determine the condition of a contaminated, compliant, or non-compliant surface; an alarm and warning system; a braking and steering system of the vehicle; and a controller. The controller may be configured to: collect data from the plurality of sensors, the data indicating the condition of a contaminated, compliant, or non-compliant surface; send the data to a neural controller having an algorithm configured to process the data. The algorithm may include: determining optimal braking and direction control instructions for the vehicle; generating warnings and alarms based on the calculated optimal braking and direction control instructions; and sending the optimal braking and direction control instructions to the braking and steering system of the vehicle and sending the warnings and alarms to an alarm and warning system of the vehicle. The controller is further configured to adjust the steering and direction control of the braking and steering system according to the optimal braking and direction control instructions provided by the neural controller.

[0028] In the foregoing device, the vehicle may be an aircraft.

[0029] In the foregoing device, the algorithm may include generating warnings and alarms, and a step of generating an arming signal for a semi-automatic or fully automatic driving system to pre-arm the steering system based on real-time conditions that may still exist when the vehicle needs to perform braking and / or directional control. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flowchart showing an adaptive braking and direction control system for a vehicle according to the present invention;

[0031] Figure 2 is showing for Figure 1 the control system shown; and

[0032] Figure 3 is showing Figure 2 the neural model of the neural controller shown. DETAILED DESCRIPTION

[0033] Now referring to the drawings, Figure 1 there is shown a flowchart that shows the operating steps of an adaptive braking and direction control system (ABADCS) for a vehicle according to the present invention. Generally, the system is initiated by a monitoring step, wherein signals provided by a plurality of sensors 10A-D (see Figure 2 ) are monitored by a neural controller 20 (see Figure 2 ).

[0034] Refer to Figure 2, the plurality of sensors may include a position sensor 10A, a non-vehicle sensor 10B, a road surface sensor 10C, and a dynamic vehicle sensor 10D. The position sensor 10A may be configured to provide real-time position data of the vehicle, such as latitude, longitude, altitude, etc. It is contemplated that such a position sensor 10A may be configured to provide a signal indicating the three-dimensional positioning of the vehicle (e.g., from a Global Positioning System (GPS)), etc.

[0035] The non-vehicle sensor 10B may be configured to provide signals indicating input from the operator, weather conditions, and obstacles in the environment. The non-vehicle sensor 10B may utilize RWIS (Road Weather Information System that provides information on residual sand, abrasives, chemicals, etc.), 3D infrastructure inputs (such as altitude, slope), and other data from autonomous vehicles and their sensors (such as LIDAR (Light Detection and Ranging), RADAR (Radio Detection and Ranging), GPS / GIS (Global Positioning System / Geographic Information System), signage, and hazard notifications, etc.) that will become increasingly available over time. It is conceivable that the other data may be provided via various communication means through a local area network and vehicle-to-vehicle network, the Internet, or the Industrial Internet of Things. In particular, vehicle sensors may provide signals indicating the position of the brake pedal, the position of the steering wheel, the amount of rotational force applied to the steering wheel, the pressure applied to the brake pedal, and the steering wheel input.

[0036] The road surface sensor 10C may be configured to provide signals indicating the condition of the surface on which the vehicle is traveling and the presence and / or condition of contaminants on that surface. In particular, the road surface sensor 10C may be configured to provide signals indicating the following information: surface temperature, ambient air temperature, surface friction, contaminant drag resistance, and rolling resistance.

[0037] The dynamic vehicle sensor 10D may be configured to provide signals indicating various dynamic movements of the vehicle, such as but not limited to vehicle speed, wheel speed, and tire angular velocity and acceleration, inertial measurements, vehicle speed, brake availability, lateral, longitudinal, and vertical speed and acceleration, axle and wheel lateral forces, brake pressure, wheel brake torque, weight on the vehicle wheels, tire pressure, lateral, longitudinal, and vertical tire forces, presence / absence of equipment attached to the vehicle and its description, sway, bump, and roll of the vehicle, static and dynamic center of gravity of the vehicle, etc.

[0038] In addition to the sensors 10A - 10D that explicitly provide information to the neural controller 20 mentioned above, it is also conceivable that other vehicle systems, not limited to the collision avoidance system, may also provide information to the neural controller.

[0039] The neural controller 20 can also be configured to receive signals indicating the commands of the vehicle driver. These signals can be supplementary to or duplicates of the information provided by the vehicle and non-vehicle sensors 10B discussed in detail above. Based on the information collected, the neural controller 20 can be programmed to determine whether there is contamination on the surface on which the vehicle is traveling. In the absence of contamination, the input from the operator is directly passed to the braking system 30 and the steering system 40 of the vehicle.

[0040] Returning Figure 1 , the signals from the aforementioned sensors are monitored by the neural controller 20. Based on these signals, the neural controller 20 is programmed to determine whether to engage the ABADCS. In particular, the ABADCS is involved when it is determined that one or more of the following have occurred: 1) the road surface condition indicates the presence of contamination on the road surface; 2) the dynamic vehicle sensor 10D provides an indication of a non-nominal response to a driver's input; 3) the dynamic vehicle sensor 10D indicates the presence of an obstacle; 4) the GIS data indicates that the vehicle is in the direction of a collision when used in combination with the dynamic vehicle sensor; or 5) any other sensor data indicates that the vehicle is out of control or has an incorrect nominal response. If none of the above occurs, the ABADCS remains inactive, but the neural controller 20 continues to monitor this data. As Figure 1 shown, when the ABADCS is bypassed, the braking output and the steering output to the vehicle remain unchanged, and the vehicle braking and steering systems 30, 40 can operate without further modification.

[0041] However, when the neural controller 20 determines that the ABADCS should be engaged, it calculates the optimal braking (t) 50 based on time and calculates the optimal steering (t) 60 based on time. It is conceivable that the ABADCS can also alert the operator that the ABADCS will / is being used through various sensory feedbacks (such as lights, vibrations, sounds, etc.).

[0042] Referring Figure 2 , the neural controller 20 takes the aforementioned sensory data as input and calculates optimized braking and steering responses based on the detected surface conditions and the steering and braking commands input by the user. Figure 3Shows how the neural controller 20 obtains the input sensory data and determines the optimal braking and steering outputs as a function of time. It is contemplated that the neural controller can be set to modify the alerts and / or add additional alerts based on the determined braking and steering commands. For example, the initial alert provided by the neural controller 20 can simply indicate to the operator that the ABADCS is active. For example, at normal acceleration, the ABADCS monitors and notes that the angular velocity of the drive wheels is significantly higher than the rate of the non-drive wheels, indicating a smooth or contaminated surface. The updated alert from the ABADCS can indicate to the user that the ABADCS is modifying the steering input 80 and / or the braking input 90 from the operator.

[0043] Reference Figure 1 , the output of the ABADCS can be used iteratively as an input back to the ABADCS to further optimize the braking and steering outputs of the ABADCS. Once the calculated braking and steering commands are optimized to a predetermined accuracy, the ABADCS can output the optimal braking, i.e., braking(t) 50, the optimal steering, i.e., steering(t) 60, and the alerts / warnings to the appropriate systems of the vehicle.

[0044] Reference Figure 1 , the optimal braking, i.e., braking(t) 50, and the optimal steering (only in the digital case), i.e., steering(t) 60, are input to the vehicle braking and steering systems 30, 40. The outputs of the vehicle braking and steering systems 30, 40 and the sensory data discussed in detail above are again evaluated by the neural controller 20, and the output of this combined evaluation is used to update the algorithm of the neural controller 20. In this regard, the neural controller 20 is a learning system configured to learn from past situations to improve its operation. It is contemplated that the neural controller 20 can be set to update its control parameters automatically or after review and approval by a designated user (e.g., a controller engineer with appropriate training and knowledge) to determine whether the parameters of the neural controller 20 should or should not be updated based on the calculations of the neural controller. In this regard, the designated user acts as a kind of gatekeeper to prevent improper modification of the neural controller.

[0045] In addition to the above, it is also contemplated that the various data collected by the ABADCS can be collected and stored in an external database 110. This data can be analyzed offline and, if appropriate, all or selected ABADCS can be updated as needed.

[0046] It is expected that the present invention can provide better braking and direction control for vehicles operating on contaminated surfaces, where the optimal friction slip values for braking and steering are different from those on uncontaminated hard surfaces. The present invention can reduce the stopping distance and improve the directional stability and control of the vehicle.

[0047] In addition to the above, current ABS and ASBS systems cannot actively integrate or adjust their responses to different surfaces through direction control. Direction control (through the tires) and the traction that needs to be shared with the braking system during simultaneous steering and braking, or the steering traction that can be optimized by threshold (and / or pedal frequency) braking, is passive. During active ABS or ASBS braking, it is low-traction, compliant, and easily yields to contaminants. This traction is usually small or almost non-existent, or exists within or on the medium where the steering action occurs. An example of this steering control traction requirement could be braking when an airplane or a car is turning on dry asphalt. Aggressive but not maximum braking may require a braking traction of 0.4g. Therefore, if the road surface and the tires can generate a traction force of 0.7g or 0.8g, first, there is no need for the ABS or ASBS system to periodically adjust the braking pressure because no skidding will occur when the anti-skid sensor is above 10% or 20% of where the ABS or ASBS is located (or the tire deceleration is above 0.40g). The ABS or ASBS signal will respectively cause the anti-lock or anti-skid valve to actuate, and the still available steering traction can allow a car that requires approximately 0.3g to execute a 50 kph (kilometers per hour), 66-meter radius turn. A second example could be a car with snow tires that "hydroplanes" in wet snow. The ABS system does not allow the tires to skid or slide over the compliant contaminants to reach a more tractionable surface that the tires would contact during driving. If it is a "non-ABS system" braking / sliding / slipping over the compliant and easily yielding snow, the tires will only generate a traction force of 0.1g when reaching the compacted snow. And the car not only takes a long time to decelerate, but it will also have almost no available lateral traction. Even when the brakes are fully released, it cannot achieve a 66-meter turn (requiring 0.107g) at a speed exceeding 30 kph. This also prevents the vehicle from decelerating and from achieving a smaller radius or a higher speed when turning with the same radius.

[0048] Therefore, there is a need for a system that addresses the above-mentioned drawbacks of the current systems.

[0049] Because ABS and ASBS systems do not sense surface contaminants and operate in the same manner in contaminants as they do on dry road surfaces, their response to contaminants is different from the way an operator looks for threshold braking and steering. For example, when the tires do not slip or slide over compliant and yielding contaminants such as wet snow to a harder surface with more traction beneath, the operator does not perform a foot brake, whereas a non-ABS braking system allows the vehicle operator to pump the brakes to a higher pressure, which results in a higher wheel deceleration, causing the tires to slip or slide in the wet snow to a higher traction surface beneath. In the case of a compliant contaminated surface, this foot-rhythm braking can provide better stopping and direction control because the intervals provide for deviations due to corrective directional instability, such as fishtailing or oversteering, while the brakes are in the "off" state, and for required directional stability, such as enhanced understeer reduction, because the vehicle can execute slower, lower lateral force turns, and enhanced steering traction, such that the tires may enjoy maximum traction slip values beneath compliant and yielding wet snow, which occurs before or after any driver-induced wheel approach or full lockup.

[0050] On an aircraft with high airspeed and ground speed with ASBS, a large portion (if not the vast majority) of the high-speed directional control of the aircraft is provided by controlling the airflow over the rudder, while the normally un-braked nose landing gear provides some directional control and stability, especially in strong crosswind conditions and at lower airspeeds and ground speeds. In some cases, when the aircraft's ASBS may not be able to obtain wheel braking available at higher non-ASBS slip values, when operating on a contaminated surface, additional or prolonged use of maximum reverse thrust may be sought for additional braking. Among several factors affecting wheel braking and directional control on a contaminated surface, poor wheel braking conditions do not transfer much weight to the nose landing gear, and aerodynamic braking is performed above the landing gear wheels, the pivot point of the weight is transferred from the braking wheels to the nose landing gear wheels, and any additional or supplementary aerodynamic aircraft braking will tend to reduce the weight on the nose landing gear. An example of why reducing wheel braking on a contaminated surface affects directional control is that the aircraft lands on wet snow in strong winds, the wheel braking is poor, and since the tires slide within or on top of the compliant and traction-yielding snow, less weight is transferred to the nose landing gear and the nose landing gear has little effect on directional control, the aircraft crew requests maximum aerodynamic braking, all braking is performed above the landing gear weight transfer pivot area, thus reducing the additional weight on the low-traction nose landing gear assembly, while the reverse thrust may block the rudder airflow, further reducing directional stability, and the strong crosswind mass affects the part of the aircraft in front of the landing gear, which can be said to be the first lever in general, when the remaining or unobstructed airflow passes through the rudder, the first type of lever action affects the directional control in front of the aircraft's center of gravity, which can be described as the third type of lever, both contribute to the front of the aircraft moving in the direction of the strong crosswind and may contribute to deviating off course to the other side of the runway, all of which are enhanced by the braking system not being able to operably identify the ground conditions and react to them.

[0051] The present invention provides a control system that senses real-time operating and ground conditions and the control and reaction of the vehicle, and optimizes wheel braking and directional control by acting differently and appropriately under different sensed conditions, thereby optimizing wheel braking and directional control, the optimization being based on all sensed and dynamically changing operating conditions, surfaces, and vehicle dynamics information, because wheel braking and directional control are controllable.

[0052] The present invention can be used under different conditions but is not limited to the following sensors: vehicle speed and wheel angular velocity and acceleration sensors, inertial measurement systems, GPS and other three-dimensional position, velocity, and position sensors, surface and ambient temperature sensors, surface and road friction measurement sensors, brake availability sensors, lateral, longitudinal, and vertical velocity and acceleration sensors, axle and wheel lateral force sensors, brake pedal and steering control position sensors, steering wheel steering force sensors, contaminant drag and rolling resistance sensors, brake pedal pressure sensors, brake pressure sensors, wheel brake torque sensors, wheel weight sensors, steering control input pressure sensors, tire pressure sensors, lateral, longitudinal, and vertical tire pressure sensors, ground vehicles, aircraft, watercraft, underground, subterranean vehicles, attachments, combination devices, trailers, and drones, sway, bump, and roll sensors, static and dynamic gravity sensors or systems, dynamic vehicle stability sensors, dynamic operator control sensors, and responses to system feedback sensors. Other non-vehicle sensing and inputs can include operator input, weather, RWIS (road weather information system, including residual sand, abrasives, and chemicals, etc.), 3D infrastructure inputs (such as elevation, slope), and other data over time from autonomous vehicles and their sensors (such as lidar, radar, GPS (Global Positioning System) / GIS (Geographic Information System), signs, and hazard notifications, etc.), and other data that can be provided via various communication means through local area networks and vehicle-to-vehicle networks, the Internet, or the Industrial Internet of Things. Other hybrid in-vehicle / extra-vehicle systems such as but not limited to collision avoidance systems can provide inputs to the ABADCS.

[0053] In the present invention, when the operator requests braking and / or direction control, the control module will poll and / or accept and / or query sensor data, algorithms, and / or available data, and the control model algorithm will output commands to the braking and / or steering system, which allows for a more comprehensive investigation or sensing of the surface on which or in which the vehicle tires operate, while investigating or sensing how the vehicle responds to the real-time output commands from the control model (see Figure 1)。An example of this operating protocol is a vehicle traveling on wet snow being braked under the control of an operator. The pedal position, pedal pressure, brake pressure, wheel speed, and wheel deceleration are sensed using sensors, and all the sensed information is monitored by a control module. From the large amount of sensing data available, the control module can detect when the tire slip increases more rapidly as the brake pressure increases, and when passing through the normal dry, hard surface skid area, but the vehicle does not decelerate as expected on an average dry hard surface and the brake pressure does not increase to the level at which the brakes attempt to decelerate when the tire slip rate is in the "optimal" dry road surface friction skid range of 10% to 20%. If the operator continues to further depress the brake pedal or increase the brake pedal pressure, the control system will identify the output of the brake pedal sensor when the driver is seeking more braking and control and command an increase in brake pressure, monitor the angular velocity deceleration of the wheels and the deceleration of the vehicle, and seek the optimal skid and brake pressure values for each wheel to provide optimal braking and directional control. The brake pressure can be adjusted at a specific frequency approaching the maximum vehicle deceleration, as long as the vehicle deceleration value remains the highest within a certain range of brake pressure and / or wheel speed modulation, and this will continue until the vehicle stops or the operator reduces the brake pedal pressure or the vehicle performs an uncontrolled operation (such as fishtailing / oversteering) or does not perform a controlled operation (such as understeering). When one of these uncontrolled events occurs, the control module has received and continues to receive the detected surface conditions (such as the maximum non-locked wheel braking availability occurs at 92% wheel slip, or there is significant contaminant wheel drag indicating a significant layer of contaminants), and the way the vehicle responds to control, and determines that the vehicle is subject to an unwanted yaw or fishtail due to a decrease in rear-wheel traction, while the vehicle's front wheels can still respond well to steering inputs. The rear-wheel brakes can be controlled as anti-lock or increased wheel braking because the system can very quickly cycle through each brake to determine the best way to regain full directional control and / or vehicle braking and / or measure and optimize overall vehicle control. That is, whether the operator control input indicates that the operator is seeking more braking than steering, more steering than braking, or generally more overall vehicle control in different situations. Example: It may be necessary to perform more steering than braking to avoid a collision (possibly detected by an autonomous vehicle sensor system such as radar), in which case a steering solution can be adopted, or it may be necessary to perform more braking than steering to avoid or mitigate a collision that cannot be avoided solely by steering.

[0054] In the present invention, sensors outputting to the control module will provide information about the surface conditions, such as available low traction (i.e., the sensed wheel angular velocity is rapidly decreasing, but the vehicle is not), vehicle dynamics (such as the weight on the wheels), and various vehicle accelerations, how to control the vehicle, such as how much braking, steering, and various vehicle accelerations are required, and how the vehicle responds to the requested control inputs.

[0055] In the present invention, the control module will interpret and evaluate the sensed information, and if the vehicle is directly responding to how the coordinating operator (human or computer operator) controls the vehicle, the control module will not modify how the vehicle's braking and direction control systems are controlled by a human or computer.

[0056] In the present invention, the control module will interpret and evaluate the sensed information, and if the vehicle is not directly responding to how the coordinating operator (human or computer operator) controls the vehicle, the control module will modify the vehicle's braking and direction control systems.

[0057] In the present invention, when needed, the control module will provide braking and direction control outputs according to threshold braking and threshold steering optimization algorithms.

[0058] According to an embodiment of the present invention, sensor output data, control output data, and any changing sensor output data and control output data will be stored on a digital storage medium and be available for download for a certain duration.

[0059] It is conceivable that sensor output data, control output data, and any changing sensor output data and control output data can be stored on a digital storage medium and be downloaded for a certain duration before being overwritten, and can be downloaded into an off-vehicle machine learning module to be analyzed, interrogated, evaluated, and / or used as needed.

[0060] It is also conceivable that sensor output data, control output data, and any changing sensor output data and control output data can be stored on a digital storage medium and be downloaded for a certain duration before being overwritten, and can be downloaded into an off-vehicle machine learning module, where it can be analyzed, interrogated, evaluated, and / or used as needed, and can be processed into an on-vehicle or off-vehicle machine learning / artificial intelligence and teaching module that can or will change the original threshold braking and threshold direction control algorithms.

[0061] In an embodiment of the present invention, sensor data can be integrated into the current or appropriately modified electronic traction and stability control system.

[0062] In another embodiment of the present invention, sensor data can be integrated into a suitable electronic traction control and electronic stability control threshold braking and direction control system, which optimizes the traction provided by the present invention to assist the tires in obtaining maximum braking and direction control traction in the currently sensed surface conditions and vehicle dynamics.

[0063] In another embodiment of the present invention, auditory, visual, tactile, and "readable" digital feedback can be provided to an individual or a computer operator, providing status feedback respectively, such as the amount of control provided compared to the requested control. This feedback can be used to familiarize the person or computer operator (through machine learning) with how to operate and control the vehicle under the observed conditions, and lead to more initial and continuous fine control under contaminated surface conditions.

[0064] In another embodiment of the present invention, the vehicle can sense and use data from on-vehicle sensors, such as drive linear torque, or compare the wheel speed with the vehicle speed to determine the real-time presence of low traction conditions (if the vehicle enables an electronic traction control (ETC) system, or similarly, by temporarily interrupting the system to monitor whether it has been enabled, the system can perform this operation), to monitor whether there is more traction and acceleration than that provided by the ETC or equivalent, and in this case, both alert the driver that the vehicle may be traveling on a low traction surface, and also notify the ABADCS that such a situation may exist, and take, for example, GIS and / or radar as an example, that the vehicle is approaching a stop sign or an obstacle, such as another vehicle. If a collision may occur if the vehicle remains on the existing driving route respectively, the operator should be warned of such a situation as early as possible, and if the operator applies the brakes early enough or hard enough to prevent the collision or stop within the required distance under the sensed conditions, the ABADCS is enabled and / or optimized to minimize the risk or harm.

[0065] It is also anticipated that data from wheel speed sensors, brake pressure sensors, axle braking force sensors, and aircraft deceleration sensors can be input into the control module, and the aircraft iteration of the present invention can limit the overall (relative to the wheels and tires) aircraft braking and overall aircraft deceleration to prevent tire damage or failure.

[0066] The present invention has been described with reference to the above exemplary embodiments. Others will make modifications and changes after reading and understanding this specification. The exemplary embodiments incorporating one or more aspects of the present invention are intended to include all such modifications and changes as long as they fall within the scope of the appended claims and their equivalents.

Claims

1. A method for controlling and optimizing the braking and direction control of a vehicle, the vehicle operating on a contaminated, compliant or non-compliant surface, the method comprises the following steps: Collect data from a plurality of sensors, the data indicating the condition of a contaminated, compliant or non-compliant surface; Send the data to a neural controller, the neural controller having an algorithm configured to process the data, wherein the algorithm comprises: Determine the optimal braking and direction control instructions for the vehicle, Generate warnings and alerts based on the calculated optimal braking and direction control instructions, and Send the optimal braking and direction control instructions to the braking and steering systems of the vehicle, and send the warnings and alerts to an alert and warning system of the vehicle; Adjust the steering and direction control of the braking and steering systems according to the optimal braking and direction control instructions provided by the neural controller; and Send a command from a user to the braking and steering systems of the vehicle to increase braking or steering beyond the optimal braking and steering instructions provided by the neural controller.

2. The method for controlling and optimizing the braking and direction control of a vehicle according to claim 1, wherein, After the step of generating warnings and alerts, there is a step of generating an arming signal for a semi-automatic or fully automatic braking system based on real-time conditions to pre-arm the braking and steering, the real-time conditions still existing when the vehicle needs to brake and / or perform direction control.

3. The method for controlling and optimizing the braking and direction control of a vehicle according to claim 1, wherein, Enable the neural controller before detecting a contaminated, compliant or non-compliant surface.

4. The method for controlling and optimizing the braking and direction control of a vehicle according to claim 3, wherein, The vehicle is one of a manual, semi-automatic and autonomous vehicle.

5. The method for controlling and optimizing the braking and direction control of a vehicle according to claim 3, wherein, The neural controller is set to repeatedly monitor the condition of a contaminated, compliant or non-compliant surface and send updated braking and direction control instructions to the braking and steering systems of the vehicle.

6. The method for controlling and optimizing the braking and direction control of a vehicle according to claim 2, wherein, The neural controller is set to repeatedly monitor the condition of a contaminated, compliant or non-compliant surface and send updated braking and direction control instructions to the braking and steering systems of the vehicle, wherein the pre-armed semi-automatic and fully automatic braking systems can be adjusted in real time according to real-time conditions.

7. The method for controlling and optimizing the braking and direction control of a vehicle according to claim 1, wherein, The neural controller is part of a network, the network comprising a plurality of neural controllers in other vehicles, the neural controller communicating with the plurality of neural controllers to relay data about contaminated surfaces.

8. The method for controlling and optimizing the braking and direction control of a vehicle according to claim 1, Characterized in that, The neural controller is part of a network that includes multiple neural controllers in other vehicles, and the neural controller communicates with the multiple neural controllers to relay alerts and warnings regarding contaminated surfaces.

9. The method for controlling and optimizing the braking and direction control of a vehicle according to claim 1, Characterized in that, The maximum braking force shall not exceed the level that causes unacceptable damage to the tires or other components of the vehicle.

10. The method for controlling and optimizing the braking and direction control of a vehicle according to claim 9, Characterized in that, The neural controller is configured to adjust the optimal braking and direction control commands to reduce the risk of tire failure.

11. The method for controlling and optimizing the braking and direction control of a vehicle according to claim 9, Characterized in that, The vehicle is an aircraft.

12. The method for controlling and optimizing the braking and direction control of a vehicle according to claim 1, Characterized in that, It further includes the step of collecting data from dynamic vehicle sensors, and the data indicates various dynamic movements of the vehicle.

13. A method for controlling and optimizing the braking and direction control of a vehicle, the vehicle operating on a contaminated, compliant or non-compliant surface, the method Comprises the following steps: Collect data from multiple sensors, and the data indicates the condition of the contaminated, compliant or non-compliant surface; Send the data to a neural controller, which has an algorithm configured to process the data, wherein the algorithm includes: Determine the optimal braking and direction control commands for the vehicle, Generate warnings and alerts according to the calculated optimal braking and direction control commands, and Send the optimal braking and direction control commands to the braking and steering systems of the vehicle, and send the warnings and alerts to an alert and warning system of the vehicle; Adjust the steering and direction control of the braking and steering systems according to the optimal braking and direction control commands provided by the neural controller; Wherein, the neural controller is part of a network that includes multiple neural controllers in other vehicles, and the neural controller communicates with the multiple neural controllers to relay data regarding contaminated surfaces; and Wherein, the neural controller is configured to use the data for the multiple neural controllers to adjust the braking and direction control commands sent to the braking and steering systems of the vehicle.

14. An apparatus for controlling and optimizing the braking and direction control of a vehicle, the apparatus Comprises: Multiple sensors, which are configured to determine the condition of the contaminated, compliant or non-compliant surface; An alert and warning system; A braking and steering system of the vehicle; And A controller, which is configured to: Collect data from the multiple sensors, and the data indicates the condition of the contaminated, compliant or non-compliant surface; Send the data to a neural controller, which has an algorithm configured to process the data, wherein the algorithm includes: Determine the optimal braking and direction control commands for the vehicle, Generate warnings and alerts based on the calculated optimal braking and direction control commands, and Send the optimal braking and direction control commands to the braking and steering systems of the vehicle, and send warnings and alerts to the alert and warning systems of the vehicle; Adjust the steering and direction control of the braking and steering systems according to the optimal braking and direction control commands provided by the neural controller; and Receive a command from a user to the braking and steering systems of the vehicle to increase braking or steering beyond the optimal braking and steering commands provided by the neural controller.

15. The apparatus for controlling and optimizing the braking and direction control of a vehicle according to claim 14, wherein, the vehicle is an aircraft.

16. The apparatus for controlling and optimizing the braking and direction control of a vehicle according to claim 14, wherein, after the step of generating warnings and alerts, the algorithm includes a step of generating an arming signal for a semi-automatic or fully automatic braking system to pre-arm the braking and steering systems based on real-time conditions still existing when the vehicle requires braking or direction control.

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