DETECTION AND MITIGATION OF IMPLOABILITIES SELECTED BY A DRIVER'S TRANSMISSION STATES

Abstract

Systems, methods, and devices for predicting unintended vehicle movement based on driver-selected transmission states and for mitigating the effects of this movement are described. In certain aspects, this includes obtaining sensor data for the vehicle, obtaining an obstacle grid for objects in the external environment, generating a self-path for the vehicle using the driver-selected transmission state, predicting unintended vehicle movement, and implementing a mitigation action determined to inhibit vehicle movement along the self-path. The sensor data includes the driver-selected transmission state of the vehicle and obstacle data for the external environment. The obstacle grid is obtained using this obstacle data.The prediction involves determining that the vehicle's natural path does not correspond to an intended path, using an unselected transmission state. The mitigation action is taken in response to a prediction of the unintended movement.

Description

INTRODUCTION

[0001] The disclosure relates to the field of driver-operated vehicles and in particular to systems and methods for detecting and mitigating implausible or unintentional transmission states selected by a driver.

[0002] There are situations in which a vehicle may be in a transmission state selected by the driver that differs from the transmission state the driver actually intends. For example, the driver may have put the vehicle in reverse and then, due to driver distraction or another event, fail to shift the vehicle into a forward gear before releasing the brake. This unintended movement can lead to unintended contact between the vehicle and surrounding objects. Therefore, there is a need in the prior art to mitigate the effects of unintended transmission states selected by a driver. SUMMARY

[0003] Systems, methods and devices according to the present disclosure provide a prediction of an unintended movement from transmission states selected by a driver and a mitigation of the effects.

[0004] Unintended movement can be predicted by detecting an unintended transmission state (e.g., reverse gear when forward gear was intended) based on the environment and / or prior driver input. In some aspects, unintended movement is predicted by generating the actual path for the vehicle and comparing it to the vehicle's apparent path based on other factors, and comparing the probabilities of potential contact between the vehicle and surrounding objects along these paths.

[0005] The systems, procedures, and devices are configured to perform one or more mitigation actions to reduce the effects of driver-initiated unintended movement events by issuing warnings or prompts, overriding driver-initiated control signals, and / or overriding driver-initiated control signals. Advantageously, mitigating or reducing the effects of driver-initiated unintended movement events by these systems, procedures, and devices optimizes the effectiveness of contact inhibition while improving the comfort and safety of vehicle drivers, passengers, and owners.

[0006] According to aspects of the present disclosure, a method comprises obtaining sensor data for a vehicle, obtaining an obstacle grid for objects in an external environment near the vehicle, generating a self-path for the vehicle using a transmission state selected by a driver, predicting unintended movement of the vehicle, and performing a mitigation action determined to inhibit movement of the vehicle along the self-path. The sensor data is obtained using a vehicle controller and includes both the driver-selected transmission state of the vehicle and obstacle data for the external environment. The obstacle grid is obtained using the controller and the obstacle data.

[0007] The vehicle's natural path is generated using the controller. The unintended movement of the vehicle is predicted using the controller. This prediction involves determining that the natural path does not correspond to an intended path of the vehicle, using an unselected transmission state. The mitigation action is then performed using the controller in response to this prediction of unintended movement.

[0008] According to further aspects of the present disclosure, the unselected transmission state is an opposite transmission state, wherein actuation of an accelerator pedal in the transmission state selected by the driver would propel the vehicle forward in a first direction, and actuation of the accelerator pedal in the opposite transmission state would propel the vehicle forward in a second direction, which is opposite to the first direction.

[0009] According to further aspects of the present disclosure, determining the unintended movement includes a determination that the intrinsic path intersects an object of the obstacle grid.

[0010] According to further aspects of the present disclosure, the method also includes determining the intended path on the basis that an available path of the vehicle for the unselected transmission state is a previous proprietary path for a previous driver-selected transmission state that immediately preceded the driver-selected transmission state.

[0011] According to further aspects of the present disclosure, the mitigation action consists of several escalating mitigation actions.

[0012] According to further aspects of the present disclosure, predicting an unintended movement of the vehicle is a determination that a probability of a faulty transmission condition exceeds a predetermined threshold, wherein the probability of the faulty transmission condition involves a comparison of probabilities for several driver action metrics and several vehicle-based metrics.

[0013] According to further aspects of the present disclosure, calculating the probability of a faulty transmission condition includes a sum of weighted probabilities of the driver action metrics and weighted probabilities of the vehicle-based metrics.

[0014] According to further aspects of the present disclosure, the driver action metrics include a gear state of the transmission, a time since the last gear change, a driver attention state, at least one driver pedal instruction, and one driver steering angle instruction.

[0015] According to further aspects of the present disclosure, the vehicle-based metrics include a distance to at least one obstacle grid, a proximity of the intrinsic path to one or more of the respective obstacle grid, a proximity of available paths to one or more of the respective obstacle grid, a speed of the vehicle, surface markings around the vehicle, and a history of the vehicle's movement.

[0016] According to aspects of the present disclosure, a system includes a controller with a processor and instructions which, when executed, cause the system to obtain sensor data for a vehicle, obtain an obstacle grid for objects in an external environment near the vehicle, generate a self-path for the vehicle using a transmission state selected by a driver, predict unintended movement of the vehicle, and perform a mitigation action determined to inhibit movement of the vehicle along the self-path. The sensor data is obtained using a controller of the vehicle and includes both the driver-selected transmission state of the vehicle and obstacle data for the external environment. The obstacle grid is obtained using the controller and the obstacle data. The self-path is generated using the controller.The unintended movement of the vehicle is predicted using the controller. This prediction involves determining that the vehicle's natural path does not correspond to its intended path, using an unselected transmission state. The mitigation action is then performed using the controller in response to this prediction of unintended movement.

[0017] According to further aspects of the present disclosure, the unselected transmission state is an opposite transmission state, wherein actuation of an accelerator pedal in the transmission state selected by the driver would propel the vehicle forward in a first direction, and actuation of the accelerator pedal in the opposite transmission state would propel the vehicle forward in a second direction, which is opposite to the first direction.

[0018] According to further aspects of the present disclosure, determining the unintended movement includes a determination that the intrinsic path intersects an object of the obstacle grid.

[0019] According to further aspects of the present disclosure, the system also includes determining the intended path based on the fact that an available path of the vehicle for the unselected transmission state is a previous proprietary path for a previous driver-selected transmission state that immediately preceded the driver-selected transmission state.

[0020] According to further aspects of the present disclosure, predicting an unintended movement of the vehicle is a determination that a probability of a faulty transmission condition exceeds a predetermined threshold, wherein the probability of the faulty transmission condition involves a comparison of probabilities for several driver action metrics and several vehicle-based metrics.

[0021] According to further aspects of the present disclosure, calculating the probability of a faulty transmission condition includes a sum of weighted probabilities of the driver action metrics and weighted probabilities of the vehicle-based metrics.

[0022] According to further aspects of the present disclosure, the driver action metrics include a gear state of the transmission, a time since the last gear change, a driver attention state, at least one driver pedal instruction, and one driver steering angle instruction.

[0023] According to further aspects of the present disclosure, the vehicle-based metrics include a distance to at least one obstacle grid, a proximity of the intrinsic path to one or more of the respective obstacle grid, a proximity of available paths to one or more of the respective obstacle grid, a speed of the vehicle, surface markings around the vehicle, and a history of the vehicle's movement.

[0024] According to aspects of the present disclosure, a vehicle includes a controller with a processor and instructions which, when executed, cause the vehicle to obtain sensor data for the vehicle, obtain an obstacle grid for objects in an external environment near the vehicle, generate a self-path for the vehicle using a transmission state selected by a driver, predict unintended movement of the vehicle, and perform a mitigation action determined to inhibit movement of the vehicle along the self-path. The sensor data is obtained using the vehicle's controller and includes both the driver-selected transmission state of the vehicle and obstacle data for the external environment. The obstacle grid is obtained using the controller and the obstacle data. The self-path is generated using the controller.The unintended movement of the vehicle is predicted using the controller. This prediction involves determining that the vehicle's natural path does not correspond to its intended path, using an unselected transmission state. The mitigation action is then performed using the controller in response to this prediction of unintended movement.

[0025] According to further aspects of the present disclosure, predicting an unintentional movement of the vehicle is a determination that a probability of a faulty transmission condition exceeds a predetermined threshold, wherein the probability of the faulty transmission condition includes a comparison of probabilities for several driver action metrics and several vehicle-based metrics, and wherein the calculation of the probability of a faulty transmission condition comprises a sum of weighted probabilities of the driver action metrics and weighted probabilities of the vehicle-based metrics.

[0026] According to further aspects of the present disclosure, the driver action metrics include a gear state of the transmission, a time since the last gear change, a driver attention state, at least one driver pedal instruction and a driver steering angle instruction, wherein the vehicle-based metrics include a distance to at least one obstacle grid, a proximity of the own path to one or more of the respective obstacle grid, a proximity of available paths to one or more of the respective obstacle grid, a vehicle speed, surface markings around the vehicle and a history of the vehicle's movement.

[0027] The features and advantages described above, and further features and advantages of the present revelation, will become apparent from the following detailed description of the best ways of carrying out the revelation when taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings are for illustrative purposes only and are not intended to limit the subject matter defined by the claims. Exemplary aspects are discussed in the following detailed description and shown in the accompanying drawings; they show: Fig. 1A- Fig. 1E an example situation of a vehicle in an unintended transmission state according to certain aspects of the present disclosure; Fig. 2 an advanced driver assistance system employed by the vehicle, in accordance with aspects of the present disclosure; Fig. 3 a first method for mitigating an unintended transmission condition of the vehicle according to aspects of the present disclosure; and Fig. 4 a second method for mitigating an unintended transmission condition of the vehicle according to aspects of the present disclosure. DETAILED DESCRIPTION

[0029] The following detailed description is merely exemplary and is not intended to limit applications and uses. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding introduction, summary, or brief description of the drawings, or in the following detailed description.

[0030] As used here, the term "module" refers to a piece of hardware, software, firmware, electronic control component, processing logic and / or processor device, individually or in combination, which includes without limitation: an application-specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated or grouped) with memory executing one or more software or firmware programs, a combinational logic circuit and / or other components providing the described functionality.

[0031] Here, embodiments of the present disclosure can be described with respect to functional and / or logical block components and various processing steps. It is acknowledged that such block components can be implemented by one or more hardware, software, and / or firmware components configured to perform the specified functions. For example, embodiments of the present disclosure can employ various components of integrated circuits (e.g., memory elements, digital signal processing elements, logic elements, lookup tables, or the like) that can perform a variety of functions under the control of one or more microprocessors or other control devices.In addition, those skilled in the art will recognize that embodiments of the present disclosure can be practiced in connection with one or more systems and that the systems described herein are merely exemplary embodiments of the present disclosure.

[0032] For the sake of brevity, techniques relating to signal processing, data fusion, signaling, control, and other functional aspects of the systems (and the individual operating components of the systems) need not be described in detail here. Furthermore, the connecting lines shown in the various figures included herein are intended to represent examples of functional relationships and / or physical couplings between the various elements. It should be noted that alternative or additional functional relationships or physical connections may exist in embodiments of this disclosure. Fig. 1A- Fig. 1E illustrates an example situation 100 of a vehicle 102 in an unintended transmission condition. Fig. Figure 1A illustrates vehicle 102 approaching an intersection 104 with a traffic control device (not illustrated). In the illustrated example, the driver does not receive the signal from the traffic control device and / or does not react to it within a reasonable time period. Fig. Figure 1B illustrates vehicle 102 after it has come to a complete stop. As can be seen, vehicle 102 came to a complete stop beyond stop line 106. To correct this, the driver shifts the transmission into reverse and maneuvers vehicle 102 backward so that it is properly positioned in the lane and behind stop line 106. Fig. Figure 1C illustrates vehicle 102 after it has returned to its proper position in the lane and behind stop line 106. While vehicle 102 is properly positioned behind stop line 106, the driver has not shifted vehicle 102 into a forward gear.

[0033] In Fig. 1D has approached a second vehicle 120 and stopped behind the first vehicle 102.

[0034] Fig. Figure 1E is an isolated view of the first vehicle 102 and the second vehicle 120, illustrating the natural path 108 and the intended path 110. As can be seen, because vehicle 102 is still in reverse, the natural path 108 lies behind vehicle 102, while the intended path 110 lies in front of vehicle 102. In particular, the natural path 108 intersects the second vehicle 120, while the intended path 110 is clear.

[0035] Advantageously, vehicle 102 is configured to mitigate the unintended transmission condition using one or more mitigation actions. These one or more mitigation actions could be, for example, a warning regarding the mismatch or active measures.

[0036] Warnings can include, for example, visual, audible, or haptic signals, combinations thereof, and the like. Visual signals can be communicated via a digital screen or a windshield display in the vehicle 102. Audible signals can be communicated via the vehicle's sound system 102. Haptic signals can originate from a vehicle component with which the driver is in contact, such as the steering wheel or the driver's seat.

[0037] Active measures can include, for example, changing an input, bypassing an input, or overwriting an input.

[0038] Modifying an input can include, for example, decreasing or increasing the response of the vehicle 102 to an input from the driver. For instance, if the vehicle 102 detects that the driver has released the brake by a predetermined amount, the vehicle 102 can allow itself to move backward slightly and apply the brake at the current pedal position as a signal to the driver that they have remained in reverse. Similarly, if the vehicle 102 detects that the driver released the brake, began to reverse, and then applied the brake again, the vehicle 102 can increase the braking force.

[0039] Overriding an input can include ignoring a change in the input. For example, if the driver releases the brake pedal, the vehicle 102 can ignore the brake pedal release and continue applying the brakes until the faulty transmission state is corrected. In an additional or alternative example, if the driver depresses the accelerator pedal, the vehicle 102 can ignore the accelerator pedal instruction and display a warning message until the faulty transmission state is corrected, or an additional driver input indicates that the transmission state is correct. The additional driver input could be, for example, releasing and re-pressing the accelerator pedal in response to the message, rejecting the message without changing the transmission state, quickly shifting out and then returning to the current transmission state, a combination of these, and the like.

[0040] Overriding an input may involve applying a corrective control action. For example, if the driver releases the brake pedal and depresses the accelerator pedal, vehicle 102 may ignore the accelerator pedal input at a predetermined point and brake again until the faulty transmission condition is corrected.

[0041] Mitigation actions can also be issued as escalating actions based on the probability of a mismatch and / or the effectiveness of issued mitigation actions. For example, vehicle 102 can start by issuing a warning, then bypass inputs if the warning is insufficient to correct the transmission state, and then override inputs if the bypass is insufficient to correct the transmission state.

[0042] Fig. Figure 2 illustrates an advanced driver assistance system (“ADAS”) 200 according to aspects of the present disclosure. The ADAS 200 includes several inputs 202, an ADAS controller 204, and several ADAS outputs 206.

[0043] Each of the multiple 202 inputs is configured to receive information for use by the ADAS controller 204. The inputs 202 can contain, for example, sensors and data. The sensors are configured to detect and signal conditions related to physical environments. The data can include, for example, data stored by the vehicle 102 or received via a data connection to the vehicle 102. In the illustrated example, the sensor inputs 202 include driver input sensors 208, touch sensors 210, individual driver adaptations 212, transmission condition sensors 214, driver monitoring system data (“DMS data”) 216, road information 218, object detection sensors 220, telematics sensors 222, and vehicle dynamics sensors 224.

[0044] The driver input sensors 208 are configured to provide information about driver inputs related to driving the vehicle 102. Driver inputs may include, for example, steering wheel position, wheel position, brake input, accelerator pedal position, combinations thereof, and the like.

[0045] The touch sensors 210 are configured to provide touch information from devices of the vehicle 102. The touch information may include information related to user interaction, e.g., with a touchscreen device, instruments in the passenger compartment, combinations thereof, and the like.

[0046] The individual driver settings 212 provide information related to the specific driver of the vehicle 102. These individual driver settings 212 may include, for example, changes to vehicle settings made by or for the driver.

[0047] The 214 transmission condition sensors are configured to provide information about the transmission's condition. This information can include, for example, the current transmission condition, historical transmission conditions, related times, combinations thereof, and the like.

[0048] The DMS data 216 contains information that has been collected and / or provided that relates to the driver's condition. This information may include, for example, attention, awareness, gaze, position, attitude, fatigue, combinations thereof, and the like.

[0049] The road information 218 is configured to provide road environment information for use by the ADAS controller 204. This road environment information can include, for example, lane markings, lane information, traffic control device availability, traffic control device status, combinations thereof, and the like.

[0050] The object detection sensors 220 are configured to detect or provide object information for the environment around the vehicle 102. This object information can include, for example, raster maps, tracking information, distance information, direction information, orientation information, classification information, combinations thereof, and the like.

[0051] The telematics sensors 222 are configured to provide telematics information to the ADAS controller 204 via communication with remote devices. This telematics information can include, for example, GPS data, imaging data, route data, position data, speed data, acceleration data, combinations thereof, and the like.

[0052] The vehicle dynamics sensors 224 are configured to provide vehicle dynamics information related to the motion of the vehicle 102. This vehicle dynamics information may include, for example, heading, speed, acceleration, roll, wheelbase, steering geometry, steering ratio, bump steering, combinations thereof, and the like.

[0053] The ADAS controller 204 is configured to support vehicle 102 operation based on received inputs 202 using one or more outputs 206. The ADAS controller 204 can be, for example, a programmable controller or an electronic control module. The programmable controller or electronic control module can be one or more microprocessors, such as a central processing unit (CPU) or a graphics processing unit (GPU), communicating with various types of computer-readable storage devices or media. Computer-readable storage devices or media can include, for example, volatile and non-volatile memory in read-only memory (ROM), read / write memory (RAM), and persistent memory (KAM). KAM is persistent or non-volatile memory that can be used to store various operating variables while the CPU is powered off.Computer-readable storage devices or storage media may be implemented using storage devices such as PROMs (programmable read-only memories), EPROMs (electrical PROMs), EEPROMs (electrically erasable PROMs), flash memory, or other electrical, magnetic, optical, or combined storage devices capable of storing data, some of which represent executable instructions used by the controller in controlling the vehicle 102.

[0054] The illustrated ADAS controller 204 includes an input interface 226, an environment processing component 228, a prediction component 230, a human-machine interface (“HMI” output interface) 232 and a vehicle control output interface 234.

[0055] The input interface 226 is configured to receive information from the inputs 202 and transmit it to other components of the ADAS controller 204. In the illustrated example, the input interface 226 includes a pedal input receiver 236, a turn signal input receiver 238, a turn signal switch input receiver 240, a driver attention receiver 242, a vehicle path prediction receiver 244, an object merging receiver 246, and a lane merging receiver 248.

[0056] The pedal input recording device 236 is configured to record data related to pedal inputs from the vehicle 102. The pedal inputs may include, for example, displacement and timing information.

[0057] The steering input acquisition device 238 is configured to record data related to steering inputs from the vehicle 102. The steering inputs may include, for example, steering wheel angle, steering actuator positions, torque or resistance to the inputs, input duration, and the like.

[0058] The turn signal switch input recording device 240 is configured to record data related to the state of the turn signal switch (e.g., positioned to indicate left, to indicate right, or in an idle position).

[0059] The driver attention recording device 242 is configured to record data related to a driver condition, such as the condition determined using the DMS data 216.

[0060] The vehicle path prediction acquisition device 244 is configured to acquire data related to a predicted vehicle path, e.g., a predicted vehicle path determined by a component of the ADAS controller 204, the vehicle 102, or a remote device.

[0061] The object merging acquisition unit 246 is configured to acquire object data collected by multiple sensors. The acquired object data can be received in a fused state, received in a raw state and merged by the object merging acquisition unit 246, forwarded to another component of the ADAS controller 204, or combinations thereof.

[0062] The lane merging acquisition device 248 is configured to acquire lane data collected by multiple sensors. The acquired lane data can be received in a fused state, received in a raw state and merged by the object merging acquisition device 246, forwarded to another component of the ADAS controller 204, or combinations thereof.

[0063] The environment processing component 228 is configured to process information and make determinations about the environment of the vehicle 102. In the illustrated example, the environment processing component 228 includes an activation state check device 250, an obstacle grid generator 252, an object heat map generator 254, and a traffic device state processing component 256.

[0064] The activation state check device 250 is configured to check the state of conditions required to enable the use of the ADAS controller 204 or components thereof. These states may include, for example, the state of one or more user settings, the presence of specific sensors and / or specific sensor data, vehicle operating conditions 102, the vehicle environment, combinations thereof, and the like.

[0065] The obstacle grid generator 252 is configured to process data and generate a grid of detected obstacles for use in determining the drivable space for the vehicle 102. The obstacle grid generator can, for example, generate an egocentric grid and / or a geocentric grid. The egocentric grid is generated relative to the vehicle 102 and can be adapted to environmental features and the vehicle's dynamics. The geocentric grid is environment-dependent and can be generated based on the vehicle's surroundings, such as an intersection or parking lot. The environment can be determined, for example, using vehicle sensors or telematics devices. After defining grid cell boundaries, detected objects can be assigned to the corresponding cells.

[0066] The object heat map generator 254 is configured to process data and generate a heat map of objects detected in the external environment. The heat map can include, for example, distance and elevation data from measurements around the vehicle 102, acquired by sensors. This data can be used by the obstacle grid generator 252, or fused with the obstacle grid by the object heat map generator 254 or another component. In some aspects, the sensors are LiDAR sensors.

[0067] The traffic device state processing component 256 is configured to process data related to the state of traffic devices. This data can include traffic device data obtained from vehicle sensors, historical information, data received from the traffic device itself, data received from other vehicles, combinations thereof, and the like.

[0068] The prediction component 230 is configured to make a prediction about the condition of the gearbox. In the illustrated example, the prediction component 230 includes a predictor 258 of the actual path, a predictor 260 of an apparent path, a generator 262 of a plausible path, a contact assessor 264, and an intention mismatch detection device 266.

[0069] The actual path predictor 258 is configured to determine the path of vehicle 102 (e.g., the eigenpath 108) when accelerating. The actual path predictor 258 can use the current transmission state of vehicle 102 as a basis.

[0070] The apparent path predictor 260 is configured to determine one or more paths intended by a driver for the vehicle 102. The apparent path predictor 260 uses one or more unselected transmission states as well as other acquired data to make the determination.

[0071] In certain aspects, predictor 260 of an apparent trajectory uses an unselected gear state, which is based on the gear state selected by a driver. For example, the unselected gear state can be a forward gear if the driver-selected gear state is reverse, a reverse gear if the driver-selected gear state is forward, and / or a park position if the driver-selected gear state is neutral.

[0072] In certain aspects, predictor 260 of an apparent path uses an unselected gear state based on a previous gear state prior to the current gear state. For example, the unselected gear state might be a forward gear if the current state is neutral and the previous state was reverse.

[0073] In certain aspects, predictor 260 of an apparent trajectory uses an unselected transmission state, which is based on the vehicle's location. For example, the unselected transmission state could be park if the current transmission state is reverse and the vehicle 102 has just entered a known parking space. The location can be determined by object detection devices, telematics devices, the absence thereof, combinations thereof, and the like.

[0074] The plausible trajectory generator 262 is configured to determine one or more plausible paths that the vehicle 102 can take, based on outputs of the apparent trajectory predictor 260 (e.g., the intended path 110) and / or the actual trajectory predictor 258 (e.g., the proper path 108).

[0075] The contact assessor 264 is configured to determine a probability that the vehicle 102, which is traveling on a path (e.g. the own path 108 and the intended path 110), will come into contact with a detected object (e.g. objects in one or more of the generated object grids).

[0076] The intention mismatch determination device 266 is configured to determine a misalignment between the natural path 108 and the intended path 110 due to a faulty gear condition.

[0077] The Intention Misalignment Determination Device 266 determines a probability of misalignment using probabilities for multiple driver action metrics and multiple vehicle-based metrics. In certain aspects, the probability of unintentional movement is determined using a sum of weighted probabilities of the driver action metrics and weighted probabilities of the vehicle-based metrics, e.g., using the following equation: P=∑WdPd+WsPs where W d Weighting factors for the probabilities for driver action metrics are; P d Probabilities for driver action metrics are; W s Weighting factors for the probabilities for vehicle-based metrics are and P s Probabilities for vehicle-based metrics are.

[0078] Driver action metrics include metrics based on specific driver actions. For example, driver action metrics can include, for instance, the gear position of the transmission. g , a time since the last gear change t g , a driver attention state e d , a gaze duration of eyes in the direction of the chosen aisle t e , an accelerator pedal instruction a d , a brake pedal instruction b d , a steering angle instruction δ d , a combination thereof and the like. The probabilities of these can be represented by the following equation: Pd=[sg˜ tg˜ eg˜ ag˜ bg˜ δg˜] where each element is a function of the effect of each respective variable on the probability. The functions can be individually weighted by the following equation: Wd=[wd1wd2⋯wdm] where each element is a scalar weight of its respective probability.

[0079] In certain aspects, the driver action metrics include a gear position of the transmission. g , a time since the last gear change t g , a steering angle instruction δ d , a driver attention state e d and an accelerator pedal instruction a d and / or a brake pedal instruction b d .

[0080] The vehicle-based metrics include metrics based on the state and history of vehicle 102, such as scene, motion, and object metrics. These vehicle-based metrics can, for example, measure distances to obstacle grids in front of the vehicle. of , distances to obstacle grids behind the vehicle d or , the proximity of an apparent path to objects in the foreground d pf , the proximity of an apparent path to objects in the background d pr , the proximity of an actual path to objects in the foreground d af, the proximity of an actual track to objects located behind it d ar , a vehicle speed v x , a distance to road markings in front of the vehicle d rf , a distance to road markings behind the vehicle d rr , road marking conditions s r , a distance to traffic control devices in front of vehicle d tf , a distance to traffic control devices behind the vehicle d tr , a traffic control device status st, a history of traffic device status changes h ts , a course of a carrier vehicle movement h m , combinations thereof and the like. The probabilities of these can be represented by the following equation: Ps=[dof˜ dor˜ dpf˜ dpr˜ daf˜ dar˜ vx˜ drf˜ drr˜ sr˜ dtf˜ dtr˜ st˜ hts˜ hm˜] where each element is a function of the effect of each respective variable on the probability. The functions can be individually weighted by the following equation: Ws=[ws1ws2⋯wsm] where each element is a scalar weight of its respective probability.

[0081] In certain aspects, a distance to at least one obstacle grid, a proximity of the own path to one or more of the respective obstacle grid, a proximity of available paths to one or more of the respective obstacle grid, the speed of the vehicle v x , surface markings around the vehicle and a path of a carrier vehicle movement h m .

[0082] The HMI output interface 232 is configured to output information to an interface system that allows an operator to view messages, issue instructions, enter information, or otherwise provide input to the system. In the illustrated example, the HMI output interface 232 includes a message controller 268 and an HMI request controller 270.

[0083] The message controller 268 is configured to generate messages for display to the driver. These messages can be visual, audible, haptic, combinations thereof, and the like. Visual messages can be presented to the driver using a screen or display device of the vehicle 102. Audible messages can be presented to the driver using the vehicle 102's sound system or a connected audio device (e.g., the driver's hearing aids). Haptic messages can be presented to the driver using surfaces with which the driver is in contact, such as the steering wheel or seat.

[0084] The HMI Request Controller 270 is configured to manage instructions, information, or inputs issued by a driver to the system in response to a system action. For example, the HMI Request Controller 270 can be an electronic controller that processes a driver input that rejects a prompt issued by the system, or a driver control action that intentionally differs from the system action. In some instances, an indication that a driver control action intentionally differs from the system action might occur in response to a prompt indicating the incorrect transmission state, the driver releasing the brake pedal, slightly reversing the vehicle, reapplying the brake, and leaving the gear selector in its current position.

[0085] The vehicle control output interface 234 is configured to output control signals to a vehicle control system that can actuate, for example, a propulsion system, a transmission system, a steering system, and a braking system of the vehicle 102. In the illustrated example, the vehicle control output interface 234 includes a steering control unit 272 and a propulsion control unit 274.

[0086] The steering control unit 272 is configured to manage a steering system of the vehicle 102. For example, the steering control unit 272 can be an electronic control module that manages the steering system by initiating a steering action in response to receiving a steering instruction, determining steering actions to be performed in response to steering instruction inputs, communicating the states of steering system components, and the like.

[0087] The propulsion control unit 274 is configured to manage a propulsion system of the vehicle 102. For example, the propulsion control unit 274 can be an electronic control module that manages the propulsion system by initiating acceleration in response to accelerator pedal input, determining acceleration actions to be performed in response to accelerator pedal control inputs, receiving messages of states from related systems (e.g., braking systems), communicating states of propulsion system components, and the like.

[0088] Fig. Figure 3 illustrates a method 300 for mitigating an unintended transmission state of the vehicle 102. In block 302, the method 300 receives sensor data for the vehicle 102. The sensor data includes a transmission state selected by a driver and obstacle data. The transmission state selected by a driver is the current setting of the vehicle 102. The obstacle data contains indications of obstacles in the environment surrounding the vehicle 102. The sensor data can be obtained using at least one controller of the vehicle 102.

[0089] In block 304, the procedure 300 receives an obstacle grid for objects in the external environment. The vehicle 102 can use at least one controller to determine or update the obstacle grid using the received object data. In block 306, the procedure 300 generates the self-path 108 for the vehicle 102 using the transmission state selected by a driver.

[0090] In block 308, procedure 300 predicts an unintended movement of the vehicle 102, which includes determining that the self-path is not aligned with an intended path of the vehicle 102, using an unselected transmission state. The unintended movement can be predicted using at least one controller of the vehicle 102.

[0091] In block 310, procedure 300 performs an attenuation action determined to inhibit movement of vehicle 102 along its own path. The attenuation action can be performed using at least one controller of vehicle 102.

[0092] Fig. Figure 4 illustrates a second method 400 for mitigating an unintended transmission state of the vehicle 102. In block 402, the method 400 is initialized with a state of the vehicle 102.

[0093] In decision 404, procedure 400 determines whether the state provides conditions that allow procedure 400 to continue. If the enabling conditions are not met, procedure 400 returns to block 402. If the enabling conditions are met, procedure 400 continues to block 406.

[0094] In block 406, the procedure 400 receives object information from sensors of the vehicle 102. In block 408, the procedure 400 generates a map of obstacles around the vehicle using the received object information.

[0095] In block 410, the method generates 400 vehicle paths using multiple transmission states for the vehicle. The transmission states include a first transmission state and a second transmission state. The first transmission state is an actual transmission state, and the second transmission state is an unselected transmission state. In some aspects, the unselected transmission state is the transmission state opposite to the direction of travel of the actual transmission state (e.g., forward versus reverse). In other aspects, the unselected transmission state is a transmission state of a position adjacent to the gear selector (e.g., for a PRNDL gear selector, "N" if the actual state is "D").

[0096] In Decision 412, Procedure 400 determines whether an action should be taken to mitigate an unintentional movement of the vehicle 102. The determination is made on the basis that the probability of an unintentional movement exceeds a predetermined threshold.

[0097] In block 414, procedure 400 performs the attenuation action. The attenuation action can be performed using at least one controller of vehicle 102.

[0098] The misalignment of the intrinsic path 108 and the intended path 110 can be actively corrected (e.g., through user interaction via a prompt) or passively corrected (e.g., through continuous monitoring that determines when the probability of misalignment between the intrinsic path 108 and the intended path 110 has fallen below a second predefined threshold). The second predefined threshold can be the same as the first predefined threshold used to trigger the mitigation action. In some aspects, the second predefined threshold is higher than the first predefined threshold.

[0099] While Fig. 1A- Fig.1E, which are created with reference to a roadway scenario, provides that the vehicle 102 can perform one or more mitigation actions to mitigate the unintended transmission state in a parking scenario. For example, if the vehicle 102 enters a parking space but does not engage the park position because the driver is distracted, the vehicle 102 can detect the unintended transmission state and perform a mitigation action to prevent the vehicle from moving forward or backward toward objects.

[0100] The condition can be detected, for example, using a driver attention history obtained via the DMS, a location determined by vehicle sensors or telematics devices, seatbelt sensors, driver movement and posture, the status of vehicle doors, a combination thereof, and the like. The mitigation action can be locking the brakes, providing notifications, and / or preventing vehicle shutdown until the vehicle 102 has been moved into a park position.

[0101] As a person skilled in the art will understand, the present disclosure is open to various modifications and alternative forms, and some representative embodiments have been shown by way of example in the drawings and described in detail above. However, it should be understood that the novel aspects of this disclosure are not limited to the specific forms illustrated in the accompanying drawings. Rather, the disclosure is intended to cover modifications, correspondences, combinations, subcombinations, permutations, groupings, and alternatives that fall within the scope and spirit of the disclosure and are defined by the accompanying claims.

[0102] As used here, and unless the context clearly indicates otherwise: the words "and" and "or" should be both connecting and separating, unless the context clearly indicates otherwise; the word "all" means "every" and the word "any" means "every"; the word "contain" means "contain without restriction" and the singular forms "a", "an" and "the" contain the plural references and vice versa.

[0103] Numerical values ​​of parameters (e.g., of quantities or conditions) in this application, unless expressly or clearly indicated otherwise in light of the context, including the appended claims, shall be understood as being modified by the expression "approximately," whether or not "approximately" actually precedes the numerical value. The numerical parameters set forth herein and in the appended claims are approximations that may vary depending on the desired properties sought to be achieved by the present disclosure. At least, and not as an attempt to limit the application of the teaching of correspondences to the scope of the claims, each numerical parameter shall be interpreted at least with respect to the number of reported significant figures and by applying conventional rounding techniques.

[0104] Words of approximation such as "approximately", "about", "essentially" and the like may be used here in the sense of, for example, "at, close or nearly at", "within 0-10% of" or "within acceptable manufacturing tolerances" or a logical combination thereof.

[0105] While the best modes for carrying out the disclosure have been precisely described, relevant experts in the field of this disclosure will recognize various alternative designs and embodiments for practicing the disclosure within the scope of the attached claims. legend

[0106] In the drawing figures, N stands for no and Y for yes.

Claims

[1] Procedure that includes: Obtained using a vehicle controller from sensor data for the vehicle, wherein the sensor data includes a driver-selected transmission state of the vehicle and obstacle data for an external environment located near the vehicle; Obtained using the controller and obstacle data of an obstacle grid for objects in the external environment; Generating a proprietary path for the vehicle using the controller and the transmission state selected by the driver; Predictions using the controller of an unintended movement of the vehicle, which includes determining that the self-path does not correspond to an intended path of the vehicle, using an unselected transmission state; and Performing, using the controller and in response to the prediction of unintended movement, a mitigation action is determined to inhibit movement of the vehicle along its own path. [2] Method according to claim 1, wherein the unselected transmission state is an opposite transmission state, actuation of an accelerator pedal in the transmission state selected by the driver would propel the vehicle forward in a first direction, and actuation of the accelerator pedal in the opposite transmission state would propel the vehicle forward in a second direction, which is opposite to the first direction. [3] Method according to claim 1, wherein determining the unintended movement includes a determination that the intrinsic path intersects an object of the obstacle grid. [4] Method according to claim 1, further comprising determining the intended path on the basis that an available path of the vehicle for the unselected transmission state is a previous proprietary path for a previous driver-selected transmission state that immediately preceded the driver-selected transmission state. [5] Method according to claim 1, wherein predicting an unintended movement of the vehicle is a determination that a probability of a faulty transmission state exceeds a predetermined threshold, and the probability of the faulty transmission state comprises a comparison of probabilities for multiple driver action metrics and multiple vehicle-based metrics. [6] Method according to claim 5, wherein the calculation of the probability of a faulty transmission condition comprises a sum of weighted probabilities of the driver action metrics and weighted probabilities of the vehicle-based metrics. [7] Method according to claim 6, wherein the driver action metrics comprise a gear state of the transmission, a time since the last gear change, a driver attention state, at least one driver pedal instruction and one driver steering angle instruction. [8] Method according to claim 7, wherein the vehicle-based metrics include a distance to at least one obstacle grid, a proximity of the intrinsic path to one or more of the at least one obstacle grid, a proximity of available paths to one or more of the at least one obstacle grid, a speed of the vehicle, surface markings around the vehicle and a trajectory of the vehicle's movement. [9] System that includes: a controller that contains a processor and instructions which, when executed, cause the system to: Receiving sensor data for a vehicle, wherein the sensor data includes a driver-selected transmission state of the vehicle and obstacle data for an external environment located near the vehicle; Obtained using obstacle data from an obstacle grid for objects in the external environment; Generating a self-path for the vehicle using the transmission state selected by the driver; Predictions of an unintended movement of the vehicle, which includes determining that the vehicle's natural path does not correspond to an intended path, using an unselected transmission state; and Taking a mitigation action in response to a prediction of unintended movement, which is determined to inhibit movement of the vehicle along its own path. [10] Vehicle comprising: a controller that contains a processor and instructions which, when executed, cause the vehicle to: Receiving sensor data for the vehicle, wherein the sensor data includes a driver-selected transmission state of the vehicle and obstacle data for an external environment located near the vehicle; Obtained using obstacle data from an obstacle grid for objects in the external environment; Generating a self-path for the vehicle using the transmission state selected by the driver; Predictions of an unintended movement of the vehicle, which includes determining that the vehicle's natural path does not correspond to an intended path, using an unselected transmission state; and Taking a mitigation action in response to a prediction of unintended movement, which is determined to inhibit movement of the vehicle along its own path.

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