Patent Description:
Embodiments relate to systems and methods for low impact crash detection for a vehicle.

In some countries, such as the United States, drivers are required under penalty of law to report accidents that result in injury or property damage, even if the injury or damage is not severe. Additionally, if the vehicle is an autonomous vehicle, the vehicle must be stopped until the accident is reported and handled by the proper authorities.

Current vehicle passive safety systems (for example, sensors and associated computers or electronic control units) to detect vehicle collisions or other safety hazards) do not have the capability to detect low impact or non-severe accidents, which creates problems for drivers who do not notice the impact or autonomous vehicles equipped with these systems. For example, current crash sensing systems for vehicles can only detect major collisions resulting in a large amount of damage, and not minor collisions (such as a bike running into a vehicle, a bumper of the vehicle gently tapping a road sign, a pedestrian hit by the vehicle, and the like).

<CIT> provides an active occupant protection system of the vehicle wherein an amount of motion associated with the impact is so low. <CIT> provides a method and apparatus for sensing a vehicle crash using signal processing. <CIT> provides an accident sensor for triggering a motor-vehicle safety system, particularly a deformable, first part and a second part that acoustically detects a deformation at the first part.

Therefore, a system is provided for detecting low impact crashes for a vehicle (such as a bike running into a vehicle, a bumper of the vehicle gently tapping a road sign, a pedestrian hit by the vehicle, and the like).

According to the present invention, a system for detecting impact crashes for a vehicle is provided as defined by independent claim <NUM>. The system includes at least one sensor, and an electronic controller configured to receive sensor data from the sensor, determine one or more features of the sensor data received from the at least one sensor, wherein the one or more features of the sensor data include an energy from one or more different frequency windows of the sensor data, determine if a collision has occurred based upon the one or more features of the sensor data by comparing the energy within the different frequency windows, and take at least one action in response to determining that the collision has occurred.

According to the present invention, a method for detecting impact collisions for a vehicle is provided as defined by independent claim <NUM>. The method includes receiving, with an electronic controller, sensor data from at least one sensor and determining, with the electronic controller, one or more features of the sensor data received from the at least one sensor, wherein the one or more features of the sensor data include an energy from one or more different frequency windows of the sensor dat. The method further includes determining, with the electronic controller, if a collision has occurred based upon the one or more features of the sensor data by comparing the energy within the different frequency windows, and taking, with the electronic controller, at least one action in response to determining that the collision has occurred.

Before any embodiments are explained in detail, it is to be understood that this disclosure is not intended to be limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. Embodiments are capable of other configurations and of being practiced or of being carried out in various ways.

A plurality of hardware and software based devices, as well as a plurality of different structural components may be used to implement various embodiments. In addition, embodiments may include hardware, software, and electronic components or modules that, for purposes of discussion, may be illustrated and described as if the majority of the components were implemented solely in hardware. However, one of ordinary skill in the art, and based on a reading of this detailed description, would recognize that, in at least one embodiment, the electronic based aspects of the invention may be implemented in software (for example, stored on non-transitory computer-readable medium) executable by one or more processors. For example, "control units" and "controllers" described in the specification can include one or more electronic processors, one or more memory modules including non-transitory computer-readable medium, one or more input/output interfaces, one or more application specific integrated circuits (ASICs), and various connections (for example, a system bus) connecting the various components.

In addition, the functionality described herein as being performed by one component may be performed by multiple components in a distributed manner. Likewise, functionality performed by multiple components may be consolidated and performed by a single component. Similarly, a component described as performing particular functionality may also perform additional functionality not described herein. For example, a device or structure that is "configured" in a certain way is configured in at least that way, but may also be configured in ways that are not listed. Furthermore, some embodiments described herein may include one or more electronic processors configured to perform the described functionality by executing instructions stored in non-transitory, computer-readable medium. Similarly, embodiments described herein may be implemented as non-transitory, computer-readable medium storing instructions executable by one or more electronic processors to perform the described functionality. As used in the present application, "non-transitory computer-readable medium" comprises all computer-readable media but does not consist of a transitory, propagating signal. Accordingly, non-transitory computer-readable medium may include, for example, a hard disk, flash memory, an optical storage device, a magnetic storage device, a ROM (Read Only Memory), a RAM (Random Access Memory), register memory, a processor cache, or any combination thereof.

<FIG> illustrates a system <NUM> for detecting low impact crashes for a vehicle <NUM> according to one embodiment. The system <NUM> includes one or more exterior microphones <NUM> and <NUM>, an interior microphone <NUM>, one or more road noise sensors <NUM>-<NUM>, one or more acceleration sensors <NUM>-<NUM>, and one or more pressure sensors <NUM> and <NUM>.

The vehicle <NUM> may be an automobile, motorcycle, tractor-trailer, truck, van, and the like. The exemplary embodiment of the vehicle <NUM> shown in <FIG> includes four wheels. However, other embodiments of the vehicle <NUM> may include less wheels (for example, a motorcycle on two wheels) or more wheels (such as a tractor-trailer with multiple wheels per axle).

The one or more exterior microphones <NUM> and <NUM> are configured to gather audio data outside the vehicle <NUM>, for example, airborne or body sounds. For example, the one or more exterior microphones <NUM> and <NUM> gather audio data of objects impacting the vehicle <NUM>. The one or more exterior microphones <NUM> and <NUM> are located, for example, at a license plate area on a front portion of the vehicle <NUM> and at a second license plate area located on a rear portion of the vehicle <NUM>.

The interior microphone <NUM> is configured to gather audio data inside the vehicle <NUM>. For example, the interior microphone <NUM> gathers audio data inside the vehicle <NUM> as an object strikes the vehicle <NUM>. The interior microphone <NUM> is located, for example, on a rearview mirror inside the vehicle <NUM>.

The one or more road noise sensors <NUM>-<NUM> are configured to utilize an accelerometer to gather low g-force data introduced by a driving surface that the vehicle <NUM> is driving on into the body of the vehicle <NUM> via wheels of the vehicle <NUM>. For example, the road noise sensors <NUM>-<NUM> are located on or near axles of the vehicle <NUM> and detect low g-force data of the wheels of the vehicle <NUM> contacting the driving surface or other driving obstacles on the driving surface (such as speed bumps, debris, and the like).

The one or more acceleration sensors <NUM>-<NUM> may be, for example, an inertial sensor configured to measure acceleration in one or more axes of movement. The one or more acceleration sensors <NUM>-<NUM> may also include a gyroscope to measure angular velocity. The one or more acceleration sensors <NUM>-<NUM> measure an acceleration and/or angular velocity of the vehicle <NUM> or portions of the vehicle <NUM> (such as a side panel, a bumper, and the like) that occur in response to a collision with an object. The one or more acceleration sensors <NUM>-<NUM> may be configured to detect low g-force accelerations, such as impacts of <NUM> to <NUM> kilometers per hour (for example, a pedestrian colliding with a side of the vehicle <NUM>). In some embodiments, the one or more acceleration sensors <NUM>-<NUM> may include a variety of sensors configured to detect different levels of g-force from impacts. In one example, the sensors are configured to detect low g-force impacts (such as the ones described above) and mid g-force impacts. Mid g-force impacts may results from objects travelling above <NUM> kilometers per hour colliding with the vehicle (for example, a motorcycle colliding with a side of the vehicle <NUM>). In one instance, the 6D cluster sensors are used to detect mid g-force impacts.

The one or more pressure sensors <NUM> and <NUM> are configured to detect pressure being applied on portions of the vehicle <NUM>. For example, the one or more pressure sensors <NUM> and <NUM> may be located on side doors of the vehicle <NUM> and configured to measure pressure applied to the side doors of the vehicle.

It is to be understood that the one or more exterior microphones <NUM> and <NUM>, the interior microphone <NUM>, the one or more road noise sensors <NUM>-<NUM>, the one or more acceleration sensors <NUM>-<NUM>, and the one or more pressure sensors <NUM> and <NUM> may be located at or on any portion of the vehicle <NUM>, and that the locations of each provided in <FIG> are part of an exemplary embodiment of the system <NUM>.

The one or more exterior microphones <NUM> and <NUM>, the interior microphone <NUM>, the one or more road noise sensors <NUM>-<NUM>, the one or more acceleration sensors <NUM>-<NUM>, and the one or more pressure sensors <NUM> and <NUM> are electrically connected to an electronic controller <NUM> and are configured to send data to the electronic controller <NUM>. An embodiment of the electronic controller <NUM> is illustrated in <FIG>.

The electronic controller <NUM> includes a plurality of electrical and electronic components that provide power, operation control, and protection to the components and modules within the electronic controller <NUM>. In the example illustrated, the electronic controller <NUM> includes an electronic processor <NUM> (such as a programmable electronic microprocessor, microcontroller, or similar device), a memory <NUM> (for example, non-transitory, computer-readable memory), and an input-output interface <NUM>. The electronic processor <NUM> is communicatively connected to the memory <NUM> and the input-output interface <NUM>. The electronic processor <NUM>, in coordination with software stored in the memory <NUM> and the input-output interface <NUM>, is configured to implement, among other things, methods described herein.

The electronic controller <NUM>, in some embodiments, may be implemented in several independent controllers (for example, programmable electronic control units) each configured to perform specific functions or sub-functions. Additionally, the electronic controller <NUM> may contain sub-modules that include additional electronic processors, memory, or application-specific integrated circuits (ASICs) for handling input-output functions, processing of signals, and application of the methods listed below. In other embodiments, the electronic controller <NUM> includes additional, fewer, or different components.

The electronic controller <NUM> may also include an integrated 6D sensor cluster <NUM>. The integrated 6D sensor cluster <NUM> includes, in one embodiment, a 3D acceleration sensor, a 3D gyroscope, and a central 2D body sound sensor. The central 2D body sound sensor detects accelerations introduced into a body of the vehicle <NUM> by different forces (such as doors opening and closing, from a driving surface.

<FIG> is a block diagram illustrating a portion of software logic <NUM> for the system <NUM> according to one embodiment. The electronic controller <NUM> receives sensor data <NUM> from a sensor (for example, the one or more acceleration sensors <NUM>-<NUM> or the one or more pressure sensors <NUM> and <NUM>), one or more vehicle parameters <NUM> received from a standalone sensor cluster or integrated in the electronic controller <NUM> (such as yaw, pitch, roll, acceleration in x, y, z axis), and audio data <NUM> and <NUM> from the one or more exterior microphones <NUM> and <NUM> (audio data <NUM>) and the interior microphone <NUM> (audio data <NUM>). The electronic controller <NUM> uses these four inputs <NUM>-<NUM> to perform contact detection using contact detection software <NUM> as described below. If contact is detected based upon the four inputs <NUM>-<NUM>, the electronic controller <NUM> is configured to take at least one action (at <NUM>).

The electronic controller <NUM> may also include extra plausibility step software. The extra plausibility step software includes instructions to process the sensor data <NUM>, vehicle parameters <NUM>, and audio data <NUM> and <NUM> to remove outlier data or perform an initial comparison of the sensor data <NUM>, vehicle parameters <NUM>, and/or audio data <NUM> and <NUM> to known collision data characteristics, or otherwise process the sensor data <NUM>, the vehicle parameters <NUM>, and the audio data <NUM> and <NUM>. For example, the electronic controller <NUM> may receive data from the one or more acceleration sensors <NUM>-<NUM> indicating an acceleration indicative of a collision, but the one or more road sensors <NUM>-<NUM> may help filter out the acceleration as noise from a pothole or a rough road, which would not be considered a low-impact collision causing injury or damage. In another embodiment, the one or more acceleration sensors <NUM>-<NUM> may detect a door slam as an acceleration, but the electronic controller <NUM> may receive data from a secondary electronic controller indicating a door was shut (for example, receiving a data flag from the secondary electronic controller indicating a Boolean value for door open, for example, <NUM> being false and <NUM> being true), and filter the detected sound out as a door slam instead of a collision.

In another embodiment, the one or more acceleration sensors <NUM>-<NUM> may detect a door slam as an acceleration, but the electronic controller <NUM> may receive data from the secondary electronic controller indicating a proximity of an object to the vehicle (for example, data from an ultrasonic sensor system or video data from a video system including one or more cameras mounted on the vehicle <NUM>). Based upon the proximity of the object, the electronic controller <NUM> is configured to increase or decrease a sensitivity of the contact detection software <NUM>. For example, if the electronic controller <NUM> determines that a second vehicle is in close proximity to the vehicle <NUM>, the electronic controller <NUM> will increase sensitivity by not filtering out a door slam (during, for example, operation of the extra plausibility step software). In this case, the door slam may be a door of the vehicle <NUM> impacting the second vehicle, which is a low-impact collision. In contrast, if no object is in close proximity to the vehicle <NUM>, the electronic controller <NUM> may decrease the sensitivity of the contact detection software <NUM> to ignore all sensor data indicative of a door slam, as the sensor data indicating the door slam will only be a door closing on the vehicle <NUM>. It is to be understood that the filtering of a door slam is only an example and that data from other sensors described in this application or from other vehicle systems could be used to increase or decrease the sensitivity of the contact detection software <NUM>.

The contact detection software <NUM> and the extra plausibility step software may be stored in the memory <NUM>.

illustrates a method <NUM> of detecting low-impact collisions for the vehicle <NUM> according to one embodiment and implemented, for example, by the contact detection software <NUM>. The method <NUM> includes receiving, with the electronic controller <NUM>, data from at least one sensor (at block <NUM>). For example, the electronic controller <NUM> may receive data from the one or more external microphones <NUM> and <NUM>, the internal microphone <NUM>, the one or more road noise sensors <NUM>-<NUM>, the one or more acceleration sensors <NUM>-<NUM>, the one or more pressure sensors <NUM> and <NUM>, or any combination of these.

In some embodiments, audio data from the one or more external microphones <NUM> and <NUM> and/or the internal microphone <NUM> or acceleration data from the one or more road noise sensors <NUM>-123is used only to validate or filter other sensor data received from a different sensor (as described below with regards to a plausibility step). In other embodiments, any impact detected as an audible noise by the one or more external microphones <NUM> and <NUM> and/or the internal microphone <NUM> is used (either alone or in conjunction with other sensor data) to confirm that a collision has occurred.

The method <NUM> also includes performing, with the electronic controller <NUM>, a plausibility step on the received sensor data (at block <NUM>). As described above with regards to the extra plausibility step software, the plausibility step is used to filter out unwanted data that could be misinterpreted as a low-impact collision. For example, as discussed above, the vehicle <NUM> may drive over a rough patch of road, and the one or more acceleration sensors <NUM>-<NUM> may detect an acceleration. When the electronic controller <NUM> receives this data, instead of immediately using it to determine if a low-impact collision has occurred, the electronic controller <NUM> utilizes the extra plausibility step software to filter out the noise of the rough patch of road (for example, by using data from the one or more road noise sensors <NUM>-<NUM>). In this way, false positives can be avoided.

The method <NUM> also includes determining, with the electronic controller <NUM>, one or more features of the sensor data (at block <NUM>). For example, the electronic controller <NUM> may determine an amplitude of the sensor data, determine a signal energy of the sensor data, determine one or more frequencies of the sensor data, perform a Fourier transform on the sensor data to obtain a frequency representation of the signal (as opposed to a time representation of the signal), determine an amount and direction of acceleration, determine an angular rotation, and the like.

In the example provided, the method <NUM> also includes determining, with the electronic controller <NUM>, if a collision has occurred (at block <NUM>). In order to determine that a collision has occurred, the electronic controller <NUM> may, in some embodiments, compare the determined features of the sensor data (from block <NUM>) to known characteristics of different impacts. The known characteristics may be data sets stored in the memory <NUM>. For example, an impact by an object at <NUM> kilometers per hour on a door of the vehicle <NUM> may have a known amplitude, set of frequencies, amount of acceleration, and the like. If the determined features match the known characteristics of this impact, the electronic controller <NUM> determines that a collision has occurred.

For example, <FIG> illustrates characteristics of different low-impact collisions <NUM> according to one embodiment. Columns <NUM>, <NUM>, and <NUM> illustrate known low-impact collisions (knocking on a door of the vehicle <NUM>, a shopping cart rolling into the vehicle <NUM>, and a screw driver scratching on a door of the vehicle <NUM>, respectively), while rows <NUM>, <NUM>, and <NUM> illustrate the characteristics of the known low-impact collisions (acceleration in g-force, rotation rate of a gyroscope, and pressure, respectively). The electronic controller <NUM> compares the received sensor data to known characteristics and, if the sensor data matches a known low-impact collision to a threshold degree in one or more characteristics, the electronic controller <NUM> determines that a collision has occurred.

In other embodiments, the electronic controller <NUM> may utilize machine learning to determine if a collision has occurred. For example, the electronic controller <NUM> may utilize a Bayes classifier with a kernel function. A Bayes classifier (or Bayesian classifier) is a type of probabilistic classifier that predicts, given the input (the determined features), a set of probabilities that different events occurred (instead of, for example, outputting the most likely event). The Bayes classifier utilizes Bayes' Theorem with strong independence assumptions of the input features. The kernel function helps focus the Bayes classifier by finding relationships between data points in data sets. The determined features are input into the Bayes classifier and, based upon the kernel function and the input features, the electronic controller <NUM> outputs a set of probabilities that different events occurred. For example, the electronic controller <NUM> may output that it is <NUM> percent likely that damage has occurred, <NUM> percent likely that contact but no damage has occurred, and <NUM> percent likely that no contact has occurred based upon the inputs being processed. A Bayes classifier may also be used to determine a location on the vehicle that damage occurred and a type of contact or damage that has occurred. <FIG> illustrates an example <NUM> of a Bayes classifier being used to determine if a collision has occurred (<NUM>), a location of the collision (<NUM>), and a type of the collision (<NUM>).

Alternatively, the electronic controller <NUM> may use a neural network to determine if a collision has occurred. The neural network is trained by feeding training data containing a number features into the network along with the outcome. The neural network has one or more nodes that process the various input features to determine an outcome, which is compared with the actual outcome to determine accuracy, and then the result of the comparison (for example, correctly identified or incorrectly identified) is back-propagated through the network to correct for any errors in the node calculations. Over iterations of time and with large, varied sets of training data (for example, a large variety of accelerations, sounds, pressures, road noises, and the like), the neural network becomes more accurate in predicting if damage occurs, the location of the damage, and the type of damage that occurs. After the neural network is trained (which may be done based upon factory tests and input into the memory <NUM> of the electronic controller <NUM>), the electronic controller <NUM> is configured to determine the features of the sensor data (at block <NUM>) and input the determined features into the neural network to receive an output indicating that damage has or has not occurred.

If the electronic controller <NUM> determines that damage has not occurred (at block <NUM>), the method <NUM> returns to waiting to receive sensor data (at block <NUM>). If the electronic controller <NUM> determines that damage has occurred, the electronic controller <NUM> takes at least one action in response (at block <NUM>). For example, the electronic controller <NUM> may be configured to output a signal to a display in the vehicle <NUM> indicating that damage has occurred (for example, output an indication that damage has occurred). If the collision is severe enough (for example, the acceleration is above a threshold based upon a location), the electronic controller <NUM> may be configured to store additional data (for example, video data from one or more cameras on the vehicle <NUM>) in the memory <NUM> or a separate memory, such as an event data recorder memory, located within the electronic controller <NUM> or in a separate electronic controller.

If the vehicle <NUM> is an autonomous vehicle, the electronic controller <NUM> may be configured to transmit a notification of damage to a remote location (such as an insurance company for a claim, to a police department, to a car dealership, to a repair facility, and the like) using a transceiver antenna, or another wireless communication device, send a signal (or a command) to a driving controller to slow the vehicle <NUM> or stop the vehicle <NUM>, and the like. The electronic controller <NUM> may also be configured to store any sensor data and associated determinations regarding low-impact collisions in the memory <NUM> for later access by a technician or other user of the vehicle <NUM>.

<FIG> illustrates a first sensor configuration <NUM> according to one embodiment. The first sensor configuration <NUM> includes the electronic controller <NUM> and peripheral contact sensors <NUM>-<NUM>. Sensors <NUM> and <NUM> are located on at the left and right front center fascia of the front bumper of the vehicle <NUM>. Sensors <NUM> and <NUM> are located at the left and right sides of the engine bay of the vehicle <NUM>. Sensors <NUM> and <NUM> are located at the left and right B pillars of the vehicle <NUM>. Pillars (A-C normally from front to rear of the vehicle <NUM>) are vertical supports for the window areas of the vehicle <NUM>. Sensors <NUM> and <NUM> are located at the left and right C pillars of the vehicle <NUM>. Sensors <NUM> and <NUM> are located at the left and right rear corners of the rear bumper of the vehicle <NUM>. Sensor <NUM> is located on the rear trunk of the vehicle <NUM>. This first sensor configuration <NUM> covers most use cases for detecting low impact collisions. By having sensor coverage at all of the locations of the peripheral contact sensors <NUM>-<NUM>, low impacts can be accurately detected at all points of the vehicle <NUM>. For example, first chart <NUM>, as shown in <FIG> and <FIG>, illustrates how impact signals are detected for different objects (a basketball, crash test dummies, and tire noise) at different impact speeds in different impact locations on the vehicle <NUM> in the first sensor configuration <NUM>.

<FIG> illustrates a second sensor configuration <NUM> according to one embodiment. The second sensor configuration <NUM> includes the electronic controller <NUM> and peripheral contact sensors <NUM>-<NUM>. Sensors <NUM> and <NUM> are located at the left and right front fascia of the front bumper of the vehicle <NUM>. Sensors <NUM> and <NUM> are located at the left and right sides of the engine bay of the vehicle <NUM>. Sensors <NUM> and <NUM> are located at the left and right C pillars of the vehicle <NUM>. Sensors <NUM> and <NUM> are located at the left and right rear corners of the rear bumper of the vehicle <NUM>. In the second sensor configuration, low impact collisions of <NUM> kilometers per hour are detected to a high degree of accuracy while requiring less sensors than the first sensor configuration <NUM>. For example, a second chart, as shown in <FIG> and <FIG>, illustrates how impact signals are detected for different objects (a basketball, crash test dummies, and tire noise) at different impact speeds in different impact locations on the vehicle <NUM> in the second sensor configuration <NUM>.

<FIG> illustrates a third sensor configuration <NUM> according to one embodiment. The third sensor configuration <NUM> includes the electronic controller <NUM> and harsh environment microphones <NUM>-<NUM>. Microphone <NUM> is located on the front bumper of the vehicle <NUM>. Microphones <NUM> and <NUM> are located on the left and right sides of the vehicle <NUM>. Microphone <NUM> is located on the rear bumper of the vehicle <NUM>. The harsh environment microphones <NUM>-<NUM> detect most of the use cases for low impact collisions, but cannot be used reliably considering audio noise spectrums from external noise, such as road noise. To better judge the external microphone audio data, an artificial intelligence algorithm, such as the above-described machine learning or Bayes classification algorithms, may be used. A third chart, as shown in <FIG> and <FIG>, illustrates how impact signals are detected for different objects (a basketball, crash test dummies, and tire noise) at different impact speeds in different impact locations on the vehicle <NUM> in the third sensor configuration <NUM>.

Claim 1:
A system (<NUM>) for detecting impact collisions for a vehicle (<NUM>), the system (<NUM>) comprising:
at least one sensor (<NUM>-<NUM>, <NUM>-<NUM>, <NUM>-<NUM>), and
an electronic controller (<NUM>) configured to
receive sensor data from the sensor,
determine one or more features of the sensor data received from the at least one sensor, wherein the one or more features of the sensor data include an energy from one or more different frequency windows of the sensor data,
determine if a collision has occurred based upon the one or more features of the sensor data by comparing the energy within the different frequency windows, and
take at least one action in response to determining that the collision has occurred.