METHOD FOR ADAPTIVE OVERLAPSE OF DRIVER AND AUTOMATED STEERING COMMANDS IN THE CASE OF EXTERNAL HAZARD

Abstract

Method (400) for controlling the steering of an autonomous vehicle (10), comprising: Operating (402) the autonomous vehicle (10) in a semi-automatic mode by a processor (44); Receiving (406) a driver input containing a measured driver torque by the processor (44); Receiving (404) hazard data by the processor (44); Determining (410) a steering input bias by the processor (44) based on an impedance relationship, impedance parameters, the measured driver torque and the hazard data; Determining (414) a reference angle by the processor (44), based on the steering command bias and a desired angle; and Generating (416) control data by the processor (44) to control the steering of the autonomous vehicle based on the reference angle, characterized in that the method includes setting the impedance parameters based on the hazard data, wherein the determination of the steering input bias is based on the set impedance parameters, the hazard data shall include at least one of the following: lateral distance to a hazard, time until impact and type of hazard, where the adjustment involves increasing the impedance stiffness parameter in a non-linear manner depending on the hazard data, wherein the hazard data contain a direction of a hazard and wherein the adjustment of the impedance parameters is based on the direction of the hazard and the steering input bias, where the adjustment involves a continuous superposition of the impedance parameters over a specific period of time, where the superposition is based on an initial time period in which a hazard occurs in the same direction as the current steering input bias, the overlapping occurs over a second period when the hazard is no longer present, and that the procedure further includes allowing driver input for direct steering of the vehicle without semi-autonomous steering when the measured driver torque is greater than a dynamic threshold.

Description

TECHNICAL AREA

[0001] The technical field generally relates to vehicles and, in particular, to methods for the semi-autonomous steering of a vehicle when an external hazard is detected. Specifically, the invention relates to a method for controlling the steering of an autonomous vehicle according to the preamble of claim 1.

[0002] Distance-dependent automated driving assistance functions serve to automatically maintain the lane and / or automatically follow the lane through steering control. With semi-autonomous steering, automatic lane keeping is achieved through steering input, while the driver occasionally initiates a steering maneuver that can counteract the automatic control measures.

[0003] To achieve a smooth transition of the driver's steering input while the automatic lane-keeping system is operating, and to maximize the controller's intervention, an impedance controller can be used to modify the steering input. In some cases, an external hazard to the vehicle may cause the driver to change the steering input. Strategies for modifying the impedance controller when an external hazard is detected are desirable to make the overall function safer and more natural for the various input torques.

[0004] Accordingly, it is desirable to provide methods and systems for semi-autonomous steering when an external hazard is present. Furthermore, other desirable features and characteristics of the present invention will become apparent from the following detailed description and the accompanying claims in conjunction with the accompanying drawings and the preceding technical field and background.

[0005] The generic patent JP 2017-189 994 A discloses a device for controlling the steering system that also reliably detects the driver's intention. Solution: An automatic steering control section comprises a calculation point for the automatic steering torque command Tm*, as well as a compensation point that calculates the final automatic steering torque command Tm** by adjusting the command Tm* based on an output of the driver intention compensation control section. The driver intention compensation control section determines the state of the driver's intention based on the impedance I, which is the differential value of the steering torques.

[0006] DE 10 2013 200 028 A1 relates to a method for situation-adaptive change of the steering stiffness in a motor vehicle, in which, for the realization of an automatic steering intervention, the underlying steering angle controller / steering angle rate controller is parameterized in such a way that the criticality of the situation is reflected in its stiffness. DESCRIPTION

[0007] The aforementioned problem is solved by a method for controlling the steering of an autonomous vehicle with the features of claim 1. Advantageous further developments are set out in the dependent claims.

[0008] In accordance with an exemplary embodiment, methods and systems for controlling the steering of an autonomous vehicle are provided. The method comprises: operation of the autonomous vehicle in a semi-automatic mode by a processor; receipt of driver input, including measured driver torque, by the processor; receipt of hazard data by the processor; determination of a steering input bias by the processor based on an impedance relationship, impedance parameters, the measured driver torque, and the hazard data; determination of a reference angle by the processor based on the steering input bias and a desired angle; and generation of control data by the processor to control the steering of the autonomous vehicle based on the reference angle.

[0009] In various embodiments, the method includes setting the impedance parameters based on the hazard data, and the determination of the steering command bias is based on the set impedance parameters.

[0010] In various embodiments, the hazard data includes at least one of the following: lateral distance to a hazard, time until impact, and type of hazard.

[0011] In various embodiments, the adjustment involves increasing the impedance stiffness parameter in a non-linear manner depending on the hazard data.

[0012] In various embodiments, the hazard data includes a direction of hazard, and the adjustment of the impedance parameters is based on the direction of the hazard and the steering command bias.

[0013] In various embodiments, the adjustment involves the continuous superposition of the impedance parameters over a specific period of time.

[0014] In various embodiments, the superposition is based on a first time period in which a hazard occurs in the same direction as the current steering command bias.

[0015] In various embodiments, the superposition takes place over a second period when the hazard is no longer present.

[0016] In various embodiments, the second time period is longer than the first. In various embodiments, the method also includes the possibility that the driver directly steers the vehicle without semi-autonomous steering if the measured driver torque is greater than a dynamic threshold.

[0017] In another embodiment, a system comprises a non-transitory, computer-readable medium containing computer instructions configured to execute a procedure; and a processor configured to execute the procedure. The procedure includes: the processor operating the autonomous vehicle in a semi-automatic mode; the processor receiving driver input, including measured driver torque; the processor receiving hazard data; the processor determining a steering input bias based on an impedance relationship, impedance parameters, the measured driver torque, and the hazard data; the processor determining a reference angle based on the steering input bias and a desired angle; and the processor generating control data to control the steering of the autonomous vehicle based on the reference angle.

[0018] In various embodiments, the method includes setting the impedance parameters based on the hazard data, with the determination of the steering input bias being based on the set impedance parameters.

[0019] In various embodiments, the hazard data includes at least one of the following: lateral distance to a hazard, time until impact, and type of hazard.

[0020] In various embodiments, the adjustment involves increasing the impedance stiffness parameter in a non-linear manner depending on the hazard data.

[0021] In various embodiments, the hazard data includes a direction of hazard, and the adjustment of the impedance parameters is based on the direction of the hazard and the steering command bias.

[0022] In various embodiments, the adjustment involves the continuous superposition of the impedance parameters over a specific period of time.

[0023] In various embodiments, the superposition is based on a first time period in which a hazard occurs in the same direction as the current steering command bias.

[0024] In various embodiments, the superposition takes place over a second period when the hazard is no longer present.

[0025] In various embodiments, the second time period is longer than the first time period.

[0026] In various embodiments, the method also includes the possibility that the driver directly steers the vehicle without semi-autonomous steering if the measured driver torque is greater than a dynamic threshold. DESCRIPTION OF THE DRAWINGS

[0027] The present disclosure is described below in conjunction with the following drawings, where identical numbers denote identical elements and where: Fig. Figure 1 is a functional block diagram of an autonomous vehicle that includes a steering control system according to exemplary embodiments; Fig. Figure 2 is a functional block diagram of an autonomous driving system of the autonomous vehicle, which includes the steering control system in accordance with various embodiments; Fig. Figure 3 is a data flow diagram showing a steering control system in accordance with exemplary embodiments; and Fig. Figure 4 is a flowchart of a process for controlling the steering of the autonomous vehicle in accordance with exemplary embodiments. DETAILED DESCRIPTION

[0028] The following detailed description is merely exemplary and is not intended to limit the disclosure or its application and use. Furthermore, there is no intention to be bound by the theory presented in the preceding background or in the following detailed description. Embodiments of the present disclosure may be described here in the form of functional and / or logical block components and various processing steps. Such block components may be implemented by any number of hardware, software, and / or firmware components configured to perform the specified functions. For example, an embodiment of the present disclosure may use various integrated circuit components, e.g.,Storage elements, digital signal processing elements, logic elements, lookup tables, or similar devices capable of performing a variety of functions under the control of one or more microprocessors or other control units. Furthermore, the person skilled in the art will recognize that embodiments of the present disclosure can be used in conjunction with any number of systems and that the systems described herein are merely exemplary embodiments of the present disclosure.

[0029] For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, and other functional aspects of the systems (and the individual operating components of the systems) are not described in detail here. Furthermore, the connecting lines shown in the various figures are intended to represent exemplary functional relationships and / or physical couplings between the different elements. It should be noted that many alternative or additional functional relationships or physical connections may exist in an embodiment of this disclosure.

[0030] In relation to Fig. 1 is a steering control system, generally represented by 100, connected to a vehicle 10 in accordance with various embodiments. Generally, the steering control system 100 modifies the driver's steering feel and reduces the steering authority of the semi-autonomous lane centering control under nominal conditions or when a lateral hazard is present (e.g., when a vehicle is in an adjacent lane, cyclists, or stationary traffic objects are present to the side). In various embodiments, the steering control system 100 adjusts the driver feel and reduces the automated steering authority to ensure a smooth connection between the driver's torque input and the automated steering while maintaining an appropriate safety distance from the hazard.

[0031] As in Fig. As shown in Figure 1, the vehicle 10 generally comprises a chassis 12, a body 14, front wheels 16, and rear wheels 18. The body 14 is mounted on the chassis 12 and essentially encloses the components of the vehicle 10. The body 14 and the chassis 12 can together form a frame. The wheels 16-18 are each rotatably connected to the chassis 12 near a corner of the body 14.

[0032] In various embodiments, the vehicle 10 is an autonomous vehicle, and the driver monitoring system 100 is integrated into the autonomous vehicle 10 (hereinafter referred to as the autonomous vehicle 10). The autonomous vehicle 10 is, for example, a vehicle that is automatically controlled to transport passengers from one place to another. The vehicle 10 is depicted as a passenger car in the embodiment shown, but it should be understood that any other vehicle, including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), watercraft, aircraft, etc., can also be used. In one exemplary embodiment, the autonomous vehicle 10 exhibits what is known as level two or three automation. As can be imagined, the autonomous vehicle 10 can have any desired level of automation in various embodiments.

[0033] As shown, the autonomous vehicle 10 generally comprises a drive system 20, a transmission system 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one control unit 34, and a communication system 36. The drive system 20 may, in various embodiments, comprise an internal combustion engine, an electric machine such as a traction motor, and / or a fuel cell drive system. The transmission system 22 is configured to transmit the power of the drive system 20 to the vehicle wheels 16-18 according to selectable speed ratios. According to various embodiments, the transmission system 22 may comprise a continuously variable automatic transmission, a continuously variable transmission, or another suitable transmission. The braking system 26 is configured to apply a braking torque to the vehicle wheels 16-18.The braking system 26 can comprise friction brakes, cable brakes, a regenerative braking system such as an electric motor, and / or other suitable braking systems in various embodiments. The steering system 24 influences the position of the vehicle wheels 16-18.

[0034] The sensor system 28 comprises one or more sensing devices 40a-40n that detect observable conditions of the external environment and / or the internal environment of the autonomous vehicle 10. The sensor devices 40a-40n may include, but are not limited to, radars, lidar, global positioning systems, optical cameras, thermal cameras, ultrasonic sensors, inertial measurement units, and / or other sensors. In various embodiments, the sensing devices 40a-40n include one or more image sensors that generate image sensor data used by the system 100.

[0035] The actuator system 30 comprises one or more actuator devices 42a-42n that control one or more vehicle functions, such as, but not limited to, the drive system 20, the transmission system 22, the steering system 24, and the braking system 26. In various embodiments, the vehicle features may also include interior and / or exterior features of the vehicle, such as doors, a trunk, and cabin features such as air conditioning, music, lighting, etc. (not numbered).

[0036] The communication system 36 is configured to wirelessly transmit information to and from other units 48, such as other vehicles (“V2V” communication), infrastructure (“V2I” communication), remote systems and / or personal devices (described in more detail in relation to Fig. 2). In an exemplary embodiment, the communication system 36 is a wireless communication system configured to communicate via a wireless local area network (WLAN) using IEEE 802.11 standards or using cellular data communication. However, additional or alternative communication methods, such as a dedicated short-range communication channel (DSRC channel), are also considered within the scope of this disclosure. DSRC channels refer to one-way or two-way short- to medium-range wireless communication channels specifically designed for use in motor vehicles, as well as to a number of protocols and standards.

[0037] The data storage device 32 stores data for use in the automatic control of the autonomous vehicle 10. In various embodiments, the data storage device 32 stores defined maps of the navigable environment. In various embodiments, the defined maps can be predefined by and obtained from a remote system (with respect to Fig. 2 (described in more detail). For example, the defined maps can be compiled by the remote system and transmitted (wirelessly and / or via cable) to the autonomous vehicle 10 and stored in the data storage device 32. In various embodiments, the defined maps include elevation maps of the environment used by the system 100. As can be seen, the data storage device 32 can be part of the control unit 34, separate from the control unit 34, or part of the control unit 34 and part of a separate system.

[0038] The control unit 34 comprises at least one processor 44 and a computer-readable storage device or medium 46. The processor 44 can be any custom or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among multiple processors connected to the control unit 34, a semiconductor-based microprocessor (in the form of a microchip or chipset), a macroprocessor, any combination thereof, or generally any instruction-executing device. The computer-readable storage devices or media 46 can include volatile and non-volatile memory, such as read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM). KAM is a persistent or non-volatile memory that can be used to store various operating variables while the processor 44 is powered off.The computer-readable storage device(s) 46 can be implemented using any number of known storage devices such as PROMs (programmable read-only memory), EPROMs (electrical PROM), EEPROMs (electrically erasable PROM), flash memory, or other electrical, magnetic, optical, or combined storage devices capable of storing data, some of which may be executable instructions used by the control unit 34 in controlling the autonomous vehicle 10.

[0039] The instructions can comprise one or more separate programs, each containing an ordered list of executable instructions for implementing logical functions. When executed by the processor 44, the instructions receive and process signals from the sensor system 28, perform logic, calculations, methods, and / or algorithms for the automatic control of the components of the autonomous vehicle 10, and generate control signals for the actuator system 30 to automatically control the components of the autonomous vehicle 10 based on the logic, calculations, methods, and / or algorithms. Although in Fig. 1 where only one control unit 34 is shown, embodiments of the autonomous vehicle 10 may include any number of control units 34 which communicate via any suitable communication medium or combination of communication media and which cooperate to process the sensor signals, perform logic, calculations, methods and / or algorithms and generate control signals to automatically control features of the autonomous vehicle 10.

[0040] In various embodiments, one or more instructions of the control unit 34 are contained in the steering control system 100 and, when executed by the processor 44, process data from the sensors and / or data within the control unit 34 to control the steering of the vehicle 10 in accordance with the exemplary embodiments disclosed herein.

[0041] In accordance with various embodiments, the control unit 34 implements an autonomous driving system (ADS) 70, as described in Fig. 2 shown. That is, suitable software and / or hardware components of the control unit 34 (e.g. the processor 44 and the computer-readable storage device 46) are used to provide an autonomous driving system 70 that is used in conjunction with the vehicle 10.

[0042] In various embodiments, the instructions of the autonomous driving system 70 can be organized according to functions, modules, or systems. As in Fig. As shown in Figure 2, the autonomous driving system 70 can, for example, comprise a computer vision system 74, a positioning system 76, a guidance system 78, and a vehicle control system 80. As can be seen, the instructions in various embodiments can be organized in any number of systems (e.g., combined, further subdivided, etc.), since the disclosure is not limited to the examples shown.

[0043] In various embodiments, the computer vision system 74 synthesizes and processes sensor data and predicts the presence, location, classification, and / or path of objects and features in the environment of the vehicle 10. In various embodiments, the computer vision system 74 can incorporate information from multiple sensors, including but not limited to cameras, lidars, radars, and / or any number of other types of sensors.

[0044] The positioning system 76 processes sensor data together with other data to determine a position (e.g., a local position relative to a map, a precise position relative to the lane of a road, the vehicle's direction, speed, etc.) of the vehicle 10 in relation to its environment. The steering system 78 processes sensor data together with other data to determine a path for the vehicle 10 to follow. The vehicle control system 80 generates control signals to steer the vehicle 10 according to the determined path.

[0045] In various embodiments, the control unit 34 implements machine learning techniques to support the functionality of the control unit 34, e.g. feature recognition / classification, obstacle avoidance, route traversal, mapping, sensor integration, determination of ground truth and the like.

[0046] As briefly mentioned earlier, the entire steering control system or parts thereof are 100% of Fig. 1 is included in ADS 70, for example as part of the vehicle control system 80. The steering control system 100, for example, generates steering command data to control a steering angle of the steering system 24. Fig. 1.

[0047] As in Fig. 3 and with continued reference to Fig. 1 and Fig. As shown in more detail in Figure 2, the steering control system 100 comprises, for example, a path control module 102, an impedance control module 104, a reference angle determination module 106, and a steering control module 108. As can be seen, the modules shown can be combined and / or further subdivided in various embodiments.

[0048] The track control module 102 receives the following inputs: road curvature data 110, vehicle speed data 112, and track error data 114. Based on these inputs, the trajectory control module determines a desired steering angle θ. d to control a vehicle's trajectory along a path and generates desired steering angle data based on this 116.

[0049] The impedance control module 104 receives left hazard data 118, right hazard data 120, and driver torque data 122 as input. In various embodiments, the left hazard data 118 indicates that a hazard has been detected on the left side of the vehicle 10, an estimated time until impact of the left hazard, and a hazard type. Similarly, the right hazard data 120 indicates that a hazard has been detected on the right lateral side of the vehicle 10, an estimated time until impact of the right hazard, and a type of hazard. The driver torque data 122 indicates the torque applied by the driver to the steering system 24. Based on the inputs, the impedance control module 104 determines a steering command bias Δθ as an impedance relationship enforced by the measured driver steering torque, τ d= MΔθ̈ + BΔθ̇ + KΔθ where M, B and K are impedance parameters: mass, damping and stiffness respectively. The impedance control module 104 generates steering bias data 124 Δθ based on the determined steering bias.

[0050] In various embodiments, the impedance control module includes a submodule 200 for parameter adjustment, which modifies one or more impedance parameters (M, B, and K) based on the detected lateral hazard. In various embodiments, the parameters are adjusted to make steering toward the side of the hazard more difficult, resulting in a return to the normal steering position (e.g., steering back to the center or to another position) when the vehicle 10 is aligned in a lane and a hazard is detected on that side.

[0051] In various embodiments, the parameters are adjusted based on the lateral distance to the hazard, the time until impact with the hazard, and / or the type of hazard. For example, the parameter adjustment module 200 increases the impedance stiffness when the steering input bias Δθ generated by the impedance control module 104 points in the same direction as the detected hazard. In various embodiments, the other impedance parameters are modified collectively to achieve the required steering bias, maintaining an appropriate damping ratio (e.g., ≥ 0.5) and a natural frequency low enough to suppress noise from the driver's torque sensor and high enough to ensure that the bandwidth is above the human actuation bandwidth (e.g., ≥ 2 Hz).

[0052] In another example, module 200 adjusts the bias parameters between steering and position nonlinearly to a high plateau, thus creating a barrier effect as the time to impact a predefined threshold decreases. Such an implementation is a paraboloid mapping for the impedance stiffness, which rises to a constantly high value as the distance to the threshold and / or the time to impact decreases towards zero, and falls to a constantly low value as it increases towards infinity.

[0053] In another example, the parameter adjustment module 200 linearly superimposes the impedance parameters when the detected hazard lies between a relevant hazard state and a target state specified by the hazard type. The parameter setting module 200 superimposes the parameters over a specific period. In various embodiments, the blending period is short when switching from the nominal condition to a relevant hazard to achieve a deflection effect, while the blending period is long when switching from a relevant hazard to a nominal hazard to achieve deadbeat steering. In various embodiments, the hazard can be classified based on the time to impact, an obstacle type or associated mass ratio, and / or the direction of movement of the hazard.

[0054] Module 106 for determining the reference angle receives as input the desired steering angle data 116 and the steering command specification data 124. Based on the inputs, the reference angle determination module 106 determines a reference angle θ. r to control the steering of the vehicle 10 and generates reference angle data 126 based on this. For example, the reference angle determination module 106 adds the steering command bias Δθ and the desired steering angle θ d to determine the steering angle reference as θ r = θ d + Δθ.

[0055] The steering control module 108 receives the reference angle data 126 and the measured steering angle data 128 as input. The measured steering angle data 128 indicates a measured steering angle of the steering system 24 of the vehicle 10. The steering control module 108 generates steering control data 130 to control the measured steering angle of the steering system 24 to or within the range of the reference angle, thereby controlling the steering of the vehicle based on a superposition of driver torque and autonomous control when a hazard is detected.

[0056] In various embodiments, the steering control module 108 generates the steering control data 130 based on the driver torque data 132 and without considering the reference angle data 126 when a driver torque override threshold is reached. The driver torque threshold can be determined depending on whether impedance control is active, the vehicle's lateral position, road curvature, hazard classification, sensor errors, map errors, lane data errors, and vehicle speed. When the driver torque is below the override threshold, the driver can shift the vehicle relative to the target curve thanks to impedance control; when the driver torque is above the override threshold, the driver has full control over maneuvering the vehicle. This results in a two-stage steering feel.

[0057] The flowchart in Fig. Figure 4 illustrates a method 400 for controlling the steering according to exemplary embodiments. The method 400 can be used in conjunction with the vehicle 10 of Fig. 1, the ADS 70 from Fig. 2 and the steering control system 100 of Fig. 3 in accordance with exemplary embodiments. As can be seen from the disclosure, the sequence of events within method 400 is not limited to those in Fig. The sequential execution shown in Figure 4 is not limited to this, but can be carried out in one or more varying sequences, as is applicable and consistent with the present disclosure. In various embodiments, the method 400 can be designed to run based on one or more predetermined events, and / or it can run continuously during the operation of the vehicle 10.

[0058] As in Fig. As shown in Figure 4, the procedure 400 can begin at 402. The left hazard data 118 and the right hazard data 120 are received at 404. The driver torque data 122 are received at 406. The impedance parameters are adjusted based on the left hazard data 118, the right hazard data 120, and the driver torque data 122, as described above. The steering input bias is determined based on the impedance relationship and the impedance parameters, for example, at 410, as described above. The desired steering angle is determined at 412 based on, for example, the vehicle speed and road characteristics. The reference angle is determined at 414 based on the steering input bias and the desired steering angle, and the steering control data is generated at 416 based on the reference angle. After that, the procedure can end at 418 (400).

[0059] Accordingly, procedures, systems, and vehicles are provided for controlling the steering of a semi-autonomous vehicle when a hazard is detected. It becomes clear that the systems, vehicles, and procedures may differ from those depicted in the figures and described here. For example, vehicle 10 of Fig. 1 and the system 100 of Fig. 3 and its components vary in different embodiments. Likewise, the steps of method 400 may differ from those in Fig. The steps of procedure 400 shown may differ from those shown and / or may be carried out simultaneously and / or in a different order than shown. Fig. The process is shown in section 4.

[0060] Although at least one exemplary embodiment has been presented in the preceding detailed description, it should be understood that there are numerous variations. It should also be noted that the exemplary embodiment or embodiments are merely examples and are not intended to limit the scope, applicability, or configuration of the disclosure in any way. Rather, the preceding detailed description is intended to provide the person skilled in the art with a practical guide for implementing the exemplary embodiment or embodiments. It is understood that various modifications to the function and arrangement of the elements can be made without departing from the scope of the disclosure as set forth in the appended claims and their statutory equivalents.

Claims

[1] Method (400) for controlling the steering of an autonomous vehicle (10), comprising: Operating (402) the autonomous vehicle (10) in a semi-automatic mode by a processor (44); Receiving (406) a driver input containing a measured driver torque by the processor (44); Receiving (404) hazard data by the processor (44); Determining (410) a steering input bias by the processor (44) based on an impedance relationship, impedance parameters, the measured driver torque and the hazard data; Determining (414) a reference angle by the processor (44), based on the steering command bias and a desired angle; and Generating (416) control data by the processor (44) to control the steering of the autonomous vehicle based on the reference angle, characterized by, that the procedure includes setting the impedance parameters based on the hazard data, wherein the determination of the steering input bias is based on the set impedance parameters, the hazard data shall include at least one of the following: lateral distance to a hazard, time until impact and type of hazard, where the adjustment involves increasing the impedance stiffness parameter in a non-linear manner depending on the hazard data, wherein the hazard data contain a direction of a hazard and wherein the adjustment of the impedance parameters is based on the direction of the hazard and the steering input bias, where the adjustment involves a continuous superposition of the impedance parameters over a specific period of time, where the superposition is based on an initial time period in which a hazard occurs in the same direction as the current steering input bias, the overlapping occurs over a second period when the hazard is no longer present, and that the procedure further includes allowing driver input for direct steering of the vehicle without semi-autonomous steering when the measured driver torque is greater than a dynamic threshold.

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