Systems and methods for real-time crash detection using telematics data

Systems, methods, and other embodiments described herein relate to improving crash prediction through correlating an observed response and a predicted response of a vehicle. In one embodiment, a method includes generating the predicted response for the vehicle as a function of a response model and according to current vehicle inputs that are control inputs associated with steering, braking, and accelerating the vehicle. The response model is a learning model that predicts behaviors of the vehicle. The method includes computing a residual indicating an extent of correlation between the predicted response and the observed response. The method includes, in response to determining the residual satisfies a crash threshold that indicates an anomaly between the predicted response and the observed response, providing an alert indicating the vehicle has likely crashed.

TECHNICAL FIELD

The subject matter described herein relates, in general, to crash prediction, and, more particularly, to identifying anomalies in the operation of a vehicle through a comparison of a predicted response extrapolated from telematics data of a vehicle and an observed response.

BACKGROUND

Vehicles may employ various safety systems to protect passengers, such as airbags, active/passive restraints, automated control assistance (e.g., anti-lock braking systems (ABS)), and so on. While these systems improve the safety of the passengers, they do not generally function to identify the occurrence of a crash. That is, the noted systems facilitate preventing crashes and/or protecting passengers against injury during a crash but may not support the identification of a crash event itself, and, thus, do not facilitate helping passengers after the crash through, for example, reliably dispatching emergency services, collecting crash data to further an understanding of how the crash occurred, and so on.

For example, in the case of airbags and/or other crash-related systems, the noted systems deploy when a specific sensor in the vehicle detects an impact. Such sensors may be placed in specific locations around the vehicle, such as in bumpers to detect physical contact beyond a threshold level, while other sensors such as accelerometers may detect generally forces imparted onto the vehicle exceeding a particular threshold. However, the noted systems generally rely on a particular detection event identified by one or more sensors. As such, the determination of a crash from the noted systems can be limited to a specific set of events such as crashes where airbags are to deploy, and so on. These circumstances correspond with a sub-set of crash events having particular characteristics. As such, there is a need for improved crash detection that reliably identifies all types of crash events.

SUMMARY

In one embodiment, example systems and methods relate to a manner of improving crash detection by using telematics data to predict vehicle responses and correlating the predicted responses with observed responses. As previously noted, reliably detecting crash events associated with a vehicle is a task that may encounter many difficulties. Therefore, an improved approach to crash detection is disclosed that leverages characteristics of telematics data to identify the occurrence of a crash instead of relying on discrete detections by particular sensors. For example, in one or more aspects, a disclosed system predicts a response of the vehicle using vehicle control inputs (e.g., steering, accelerator inputs, braking inputs) to generate a baseline for determining whether behaviors of the vehicle correspond with anomalies that may be indicative of a crash.

This predicted response identifies a behavior of the vehicle that is expected according to the inputs, and, for example, current dynamics of the vehicle. Thus, the predicted response is an expected behavior as determined, in one approach, according to a model that uses the noted data elements from the vehicle as inputs. As such, once the vehicle progresses to a temporal point corresponding with the predicted response, the system can then compare the predicted response with an observed response that identifies how the vehicle actually behaved over the subject time period. If, for example, the residual (e.g., difference or time series of differences) between the predicted and observed responses satisfies a threshold, then the system may conclude that the vehicle has encountered an anomaly in operation that may correlate with a crash. In one approach, the system can further validate the residual by, for example, analyzing sensor data about operation of the vehicle from a time after the occurrence of the anomaly.

This additional analysis may include analyzing sensor data about the operation of the vehicle during a time subsequent to the observed response, and in relation to a set of rules that define additional validating aspects of behaviors of the vehicle. Accordingly, in response to the residual satisfying the crash threshold and, thus, the identification of a crash event, the system may generate an alert. In various approaches, the alert may take different forms, but generally includes notifying emergency services, recording/communicating data about the event, and so on. In this way, the disclosed system improves the identification of crash events through analysis of available telematics data, thereby furthering the safety of the vehicle and passengers riding therein.

In one embodiment, a crash prediction system for improving crash prediction through correlating an observed response and a predicted response of a vehicle is disclosed. The crash prediction system includes one or more processors and a memory communicably coupled to the one or more processors. The memory stores a prediction module including instructions that when executed by the one or more processors cause the one or more processors to generate the predicted response for the vehicle as a function of a response model and according to current vehicle inputs that are control inputs associated with steering, braking, and accelerating the vehicle. The response model is a learning model that predicts behaviors of the vehicle. The memory stores a determination module including instructions that when executed by the one or more processors cause the one or more processors to compute a residual indicating an extent of correlation between the predicted response and the observed response. The determination module includes instructions to, in response to determining the residual satisfies a crash threshold that indicates an anomaly between the predicted response and the observed response, provide an alert indicating the vehicle has likely crashed.

In one embodiment, a non-transitory computer-readable medium for improving crash prediction through correlating an observed response and a predicted response of a vehicle and including instructions that, when executed by one or more processors, cause the one or more processors to perform one or more functions is disclosed. The instructions include instructions to generate the predicted response for the vehicle as a function of a response model and according to current vehicle inputs that are control inputs associated with steering, braking, and accelerating the vehicle. The response model is a learning model that predicts behaviors of the vehicle. The instructions include instructions to compute a residual indicating an extent of correlation between the predicted response and the observed response. The instructions include instructions to, in response to determining the residual satisfies a crash threshold that indicates an anomaly between the predicted response and the observed response, provide an alert indicating the vehicle has likely crashed.

In one embodiment, a method for improving crash prediction through correlating an observed response and a predicted response of a vehicle is disclosed. In one embodiment, the method includes generating the predicted response for the vehicle as a function of a response model and according to current vehicle inputs that are control inputs associated with steering, braking, and accelerating the vehicle. The response model is a learning model that predicts behaviors of the vehicle. The method includes computing a residual indicating an extent of correlation between the predicted response and the observed response. The method includes, in response to determining the residual satisfies a crash threshold that indicates an anomaly between the predicted response and the observed response, providing an alert indicating the vehicle has likely crashed.

DETAILED DESCRIPTION

Systems, methods, and other embodiments associated with a manner of improving crash detection by predicting vehicle responses are disclosed. As previously noted, reliably detecting crash events associated with a vehicle is a task that may encounter various different difficulties. For example, existing safety systems may provide for detecting only events having particular characteristics (e.g., head on crashes exceeding a threshold speed that trigger physical crash sensors). Further difficulties may also occur in relation to using specific event information such as individual acceleration/deceleration events from an inertial measurement unit (IMU). For example, specific instances of peak threshold acceleration/deceleration may be associated with a strong braking, accelerating, or skidding event that do not necessarily correspond with a crash, yet various systems may still rely on such indicators in isolation, thereby resulting in false positives. As such, the determination of a crash from the noted systems is generally limited to specific types of crash events and, thus, may result in the detection of a limited set of crashes while failing to detect other occurrences or detecting non-crash events as false positives.

Therefore, in one aspect, the present approach improves crash detection by using multiple data sources (e.g., telematics data) to predict vehicle responses and correlating the predicted responses with observed responses thereby identifying when the vehicle is operating as expected or not to better detect occurrences of anomalous events (e.g., crashes). In one or more aspects, a disclosed crash prediction system uses sensor data (e.g., telematics data) that includes vehicle control inputs (e.g., steering, accelerator inputs, and braking inputs) and other information (e.g., dynamics) to predict a response of the vehicle. For example, as the vehicle proceeds along a path, the crash detection system may iteratively calculate the predicted response as a guidepost for identifying expected behavior of the vehicle over a defined temporal horizon (e.g., 1-2 seconds).

Accordingly, in one embodiment, the crash prediction system implements a model (e.g., a learning model) that accepts sensor data (e.g., vehicle control inputs, dynamics data, etc.) from the vehicle as an electronic input and produces the predicted response as an output. The predicted response may identify predicted yaw rates, lateral acceleration, longitudinal acceleration, and/or other operating characteristics of the vehicle at a subsequent time step. As the vehicle progresses to the subsequent time step, the crash prediction system can then observe actual responses of the vehicle. The crash prediction system generates the observed response from sensor data embodying the operation of the vehicle over the time step that can then serve as a point of comparison against the predicted response.

As such, the crash prediction system can then compare the predicted response with the observed response to generate a residual value that identifies an extent of correlation between the predicted and observed response. If the vehicle is operating as expected and has not encountered any anomalies (e.g., unexpected events such as a crash), then the predicted and observed response should closely correlate. However, if, for example, the residual satisfies a crash threshold (e.g., exceeds a defined difference/variance in a single value or a series of values over a time window) that indicates a lack of correspondence, then the crash prediction system concludes, in one approach, that the vehicle has encountered an anomaly in operation that may correlate with a crash.

Consequently, in at least one embodiment, to further validate the potential crash event, the crash prediction system performs a further analysis by analyzing sensor data about the operation of the vehicle from a time after the occurrence of the anomaly. This additional analysis may include analyzing sensor data about operation of the vehicle during a time subsequent to the observed response and in relation to a set of rules that define additional validating aspects of behaviors of the vehicle. In various approaches, the set of rules may include additional behavior identifications such as whether the vehicle has stopped moving, detection of peak acceleration forces, iterative occurrences of anomalies, activation of vehicle safety systems (e.g., traction control, ABS, etc.), and so on.

In any case, once identified, the crash prediction system may generate an alert about the crash event to facilitate a response and/or log information about the event for subsequent analysis. In various approaches, the alert may take different forms but generally includes notifying emergency services, recording/communicating data about the event, and so on. In this way, the disclosed system improves the identification of crash events through analysis of available telematics data thereby furthering safety of the vehicle and passengers riding therein.

Referring toFIG.1, an example of a vehicle100is illustrated. As used herein, a “vehicle” is any form of powered transport. In one or more implementations, the vehicle100is an automobile. While arrangements will be described herein with respect to automobiles, it will be understood that embodiments are not limited to automobiles. In some implementations, the vehicle100may be any device that, for example, transports passengers and includes the noted sensory devices from which the disclosed predictions and determinations may be generated.

The vehicle100also includes various elements. It will be understood that in various embodiments, it may not be necessary for the vehicle100to have all of the elements shown inFIG.1. The vehicle100can have any combination of the various elements shown inFIG.1. Further, the vehicle100can have additional elements to those shown inFIG.1. In some arrangements, the vehicle100may be implemented without one or more of the elements shown inFIG.1. While the various elements are shown as being located within the vehicle100inFIG.1, it will be understood that one or more of these elements can be located external to the vehicle100. Further, the elements shown may be physically separated by large distances. For example, as discussed, one or more components of the disclosed system can be implemented within a vehicle while further components of the system are implemented within a cloud-computing environment, as discussed further subsequently.

Some of the possible elements of the vehicle100are shown inFIG.1and will be described along with subsequent figures. However, a description of many of the elements inFIG.1will be provided after the discussion ofFIGS.2-7for purposes of brevity of this description. Additionally, it will be appreciated that for simplicity and clarity of illustration, where appropriate, reference numerals have been repeated among the different figures to indicate corresponding or analogous elements. In addition, the discussion outlines numerous specific details to provide a thorough understanding of the embodiments described herein. Those of skill in the art, however, will understand that the embodiments described herein may be practiced using various combinations of these elements. In any case, as illustrated in the embodiment ofFIG.1, the vehicle100includes a crash prediction system170that is implemented to perform methods and other functions as disclosed herein relating to improving crash detection in a vehicle. As will be discussed in greater detail subsequently, the crash prediction system170, in various embodiments, may be implemented partially within the vehicle100and may further exchange communications with additional aspects of the system170that are remote from the vehicle100in support of the disclosed functions. Thus, whileFIG.2generally illustrates the system170as being self-contained, in various embodiments, the system may be implemented within multiple separate devices some of which may be remote from the vehicle100.

With reference toFIG.2, one embodiment of the crash prediction system170ofFIG.1is further illustrated. The crash prediction system170is shown as including a processor110from the vehicle100ofFIG.1. Accordingly, the processor110may be a part of the crash prediction system170, the crash prediction system170may include a separate processor from the processor110of the vehicle100, and/or the crash prediction system170may access the processor110through a data bus or another communication path. In further aspects, the processor110is a cloud-based resource that communicates with the system170through a communication network. In one embodiment, the crash prediction system170includes a memory210that stores a prediction module220and a determination module230. The memory210is a random-access memory (RAM), read-only memory (ROM), a hard-disk drive, a flash memory, or other suitable memory for storing the modules220and230. The modules220and230are, for example, computer-readable instructions within the physical memory210that when executed by the processor110cause the processor110to perform the various functions disclosed herein.

The crash prediction system170may be further implemented as a cloud-based system that functions within a cloud-computing environment300as illustrated in relation toFIG.3. That is, for example, the crash prediction system170may acquire telematics data (i.e., sensor data250) from vehicles and execute as a cloud-based resource that is comprised of devices (e.g., distributed servers) remote from the vehicle100to predict responses and determine when a vehicle has encountered a crash. Accordingly, the crash prediction system170may communicate with vehicles (e.g., vehicles310,320, and330) that are geographically distributed. In one approach, the cloud-based crash prediction system170collects the sensor data250from components or separate instances of the system170that are integrated with the vehicles310-330. In one aspect, communications between the cloud300and the vehicles310-330may function as heartbeat signals to identify to the cloud300that the vehicles310-330are still operating.

Of course, along with the communications, the vehicles310-330provide the sensor data250. As such, the cloud-based aspects of the system170may then process the sensor data250separately for the vehicles310-330to determine the differences between the predicted and observed responses. Of course, in further aspects, the vehicle-based systems may perform part of the processing while the cloud-computing environment300may handle a remaining portion or function to validate results of the vehicle-based systems170. It should be appreciated that apportionment of the processing between the vehicle and the cloud may vary according to different implementations. Additional aspects of the cloud computing environment300will be discussed in relation to components of the system170andFIG.2.

Continuing withFIG.2, in one embodiment, the prediction module220includes instructions that function to control the processor110to generate a predicted response for the vehicle100as a function of a response model260and according to at least current vehicle inputs that are control inputs. The predicted response of the vehicle100indicates the expected behavior of the vehicle100according to the inputs over a defined time period into the future. The defined time period is, in one embodiment, 0.5 seconds into the future beyond a point in time for which the prediction module220has current sensor data250. Of course, in further implementations, the prediction module220can be implemented to generate the predicted response according to different temporal horizons such as one, two, three, or more seconds into the future, which may be associated with varying degrees of error.

The predicted response itself defines, in one or more approaches, operating characteristics, which are generally dynamics of the vehicle100, associated with steering, braking, and accelerating the vehicle100. That is, the predicted response may include lateral acceleration, longitudinal acceleration, and a yaw rate. In further approaches, the predicted response may include further operating characteristics such as pitch rate, roll rate, and vertical acceleration (e.g., gravitational-axis component accelerations such as additional downforce or negative g-forces). In any case, the response model260processes various electronic inputs acquired from the vehicle100to provide the predicted response.

The various electronic inputs originate from the vehicle100, which may be stored in a data store240of the crash prediction system170. Accordingly, in one embodiment, the crash prediction system170includes the data store240. The data store240is, in one embodiment, an electronic data structure (e.g., a database) stored in the memory210or another data store and that is configured with routines that can be executed by the processor110for analyzing stored data, providing stored data, organizing stored data, and so on. Thus, in one embodiment, the data store240stores data used by the modules220and230in executing various functions. In one embodiment, the data store240includes the sensor data250along with, for example, response model260and or other information that is used by the modules220and230.

Accordingly, the prediction module220and/or the determination module230, in one embodiment, control respective sensors (e.g., IMU, input sensors, etc.) of the vehicle100to provide the data inputs in the form of the sensor data250. Additionally, while the modules220/230are discussed as controlling the various sensors to provide the sensor data250, in one or more embodiments, the modules220/230can employ other techniques to acquire the sensor data250that are either active or passive. For example, the prediction module220may passively sniff the sensor data250from a stream of electronic information provided by the various sensors to further components within the vehicle100. Moreover, the modules220/230can undertake various approaches to fuse data from multiple sensors when providing the sensor data250. Thus, the sensor data250, in one embodiment, represents a combination of perceptions acquired from multiple sensors.

In general, the sensor data250includes at least the vehicle control inputs. The vehicle control inputs comprise, in one example, steering inputs (e.g., steering wheel angle, rate and direction of rotation, etc.), braking inputs (e.g., extent of brake pedal activation/pressure), and acceleration inputs (e.g., extent of accelerator pedal activation/pressure). In further aspects, the vehicle control inputs also specify transmission control inputs (e.g., gear selection), drive mode (e.g., 2-wheel drive, 4-wheel drive), engine/motor parameters (e.g., engine RPM, driving mode for hybrid vehicles, etc.), and so on. In yet further aspects, the sensor data250includes current dynamics data such as angular velocity, g-forces (e.g., longitudinal, lateral), speed profile, wheel speeds, activation controls (e.g., ABS activation, traction control activation, stability control activation, etc.), and so on.

Of course, depending on the sensors that the vehicle100includes, the available information that system170can harvest as the sensor data250may vary. As one example, according to a particular implementation, the vehicle100may include different versions of an IMU sensor that are separately capable of different measurements. That is, in one implementation, the IMU sensor may provide yaw rate, lateral acceleration, and longitudinal acceleration, whereas, in a separate implementation with a more robust IMU sensor, the IMU sensor may provide additional data such as pitch rates, roll rates, vertical acceleration, etc. As such, the modules220/230may, in one or more approaches, be configured to adapt to different electronic inputs depending on the availability of such information. Thus, as will be discussed in greater detail subsequently, the prediction module220may generate the predicted response with additional points of comparison when, for example, additional sensor inputs are available. As an additional note, telematics data as used herein generally encompasses the sensor data250and may include further information such as vehicle identifiers, location information (e.g., GPS position), and other information that may be useful in communicating with the cloud300or other entities for purposes of generating and providing alerts.

In any case, the prediction module220uses the response model260to generate the predicted response from at least a portion of the sensor data250. In one embodiment, the response model260is a learning model that predicts behaviors of the vehicle100. It should be appreciated that the prediction module220, in combination with the response model260, can form a computational model such as a machine learning model/algorithm, deep learning model, a neural network model, or another similar approach. In one embodiment, the response model260is a statistical model such as a regression learning model (e.g., multiple linear regression model, random forest, nonlinear regression, etc.) that estimates values of the vehicle response out to a time horizon. Accordingly, the response model260can be a polynomial regression (e.g., least weighted polynomial regression, multiple linear regression), least squares or another suitable approach.

Moreover, in alternative arrangements, the response model260is a probabilistic approach, such as a hidden Markov model. In either case, the prediction module220, when implemented as a machine learning model or another model, in one embodiment, electronically accepts the sensor data250as an input. Accordingly, the prediction module220in concert with the response model260produce various determinations/assessments as an electronic output that characterize the noted aspects as, for example, separate electronic values. Additionally, as a further aspect, the system170may train the response model260to learn various parameters (e.g., hyper-parameters, coefficients, etc.) through a supervised learning process (i.e., with labeled data) or as may be otherwise suitable. Accordingly, in one or more aspects, the sensor data250may be logged, correlated with known crash events, and used to train and/or retrain the response model260. As an additional note, the training may occur locally within the vehicle100or as a separate pre-configuration process in the cloud300. Thus, in one embodiment, the response model260is trained according to specifics of the vehicle100itself including whether the vehicle is hauling a trailer, size/type of wheels (e.g., after-market modified tires/wheels versus manufacturer wheels), adaptations to aerodynamics of the particular vehicle (e.g., bike racks, spoilers, body kits, etc.), engine tuning, and other aspects that influence the dynamics of the vehicle100. Moreover, in alternative arrangements, the response model260is a probabilistic approach, such as a hidden Markov model. In either case, the prediction module220, when implemented as a machine learning model or another model, in one embodiment, electronically accepts the sensor data250as an input. Accordingly, the prediction module220in concert with the response model260produce various determinations/assessments as an electronic output that characterize the noted aspects as, for example, separate electronic values. Additionally, as a further aspect, the system170may train the response model260to learn various parameters (e.g., hyper-parameters, coefficients, etc.) through a supervised learning process (i.e., with labeled data) or as may be otherwise suitable. Accordingly, in one or more aspects, the sensor data250may be logged, correlated with known crash events, and used to train and/or retrain the response model260. As an additional note, the training may occur locally within the vehicle100or as a separate pre-configuration process in the cloud300. Thus, in one embodiment, the response model260is trained according to specifics of the vehicle100itself including whether the vehicle is hauling a trailer, size/type of wheels (e.g., after-market modified tires/wheels versus manufacturer wheels), adaptations to aerodynamics of the particular vehicle (e.g., bike racks, spoilers, body kits, etc.), engine tuning, and other aspects that influence the dynamics of the vehicle100.

Continuing with the discussion ofFIG.2, the response model260, in one or more embodiments, may include separate sub-models associated with the separate aspects of the predicted response. In one approach, the response model260includes a separate output-specific model for each separate component of the predicted response. Thus, in an instance where the prediction module220is to generate the predicted response with a yaw rate, lateral acceleration, and longitudinal acceleration, the response model260includes a separate yaw model that predicts the yaw rate, a separate lateral model that predicts the lateral acceleration, and a separate longitudinal model that predicts the longitudinal acceleration of the vehicle100. In further aspects, the response model260includes further sub-models where the predicted response is to include further modeled aspects of the vehicle100.

In yet a further aspect of the response model260, the response model260, in at least one embodiment, is a situation-specific model. In other words, depending on characteristics of the current sensor data250, the prediction model220, for example, selects a different response model260from a set of situational models. By way of example, the prediction module220, in one approach, selects the response model260from the set according to current combinations of vehicle control inputs. Thus, the prediction module220may select a first model when the current vehicle inputs include only accelerator pedal input (i.e., neutral steering and no braking input), a second model when the current vehicle inputs include only brake pedal input, i.e., neutral steering and no accelerator input), and a third model associated with when the current vehicle inputs indicate no values for either the braking input and the accelerator input.

It should be appreciated that while three separate examples are provided, the prediction module220can use different combinations of the vehicle control inputs and other operating characteristics of the vehicle100embodied in the sensor data250to identify and select a particular response model260to use when generating the predicted response. Moreover, the prediction module220generally selects the response model260with, for example, each separate observation of the sensor data250. Thus, as the prediction module220generates subsequent predictions, the module220undertakes the selection process for the response model260according to separate acquisitions of the sensor data250.

Continuing withFIG.2, in one embodiment, the determination module230includes instructions that, when executed by the one or more processors110, cause the one or more processors110to compute a residual indicating an extent of correlation between the predicted response and the observed response. However, in order to compute a value for the residual, the determination module230first acquires the observed response of the vehicle100. Thus, as previously outlined, the determination module230acquires the sensor data250for a time at the temporal horizon that corresponds with a point in time of the predicted response. Consequently, the determination module230acquires the sensor data250subsequent to the sensor data250used to compute the predicted response (e.g., 0.5 seconds after). Furthermore, the determination module230may generate the residuals over a time series (e.g., a sliding window of time) and provide the residuals as a characterization of operation for a given time window.

Similar to the predicted response, the observed response is comprised of specific operating characteristics of the vehicle100. As a general principle, the determination module230acquires the sensor data250and generates the observed response to mirror the predicted response in the elements included therein. Thus, where the predicted response includes the yaw rate, the lateral acceleration, and the longitudinal acceleration of the vehicle100, the determination module230similarly generates the observed response from the sensor data250to include matching elements. As such, when the prediction module220generates the predicted response to include further or fewer elements, the determination module230matches the generated elements in order to facilitate the subsequent comparison.

Accordingly, once the determination module230generates the observed response, the determination module230proceeds with computing the residual. In one embodiment, the determination module230generates the residual by comparing the observed response with the predicted response on an element by element basis. The comparison may include a direct differencing operation (e.g., observed response−predicted response=residual) or a more complex heuristic that separately weighs the different elements, combines separate differences for the elements into a single residual value (e.g., a weighted average), or involves other statistical tests based on norm thresholding (e.g., norm 1, 2, or infinity).

The determination module230can then use the residual to determine whether the predicted response and the observed response correspond or not through a further comparison with a crash threshold. In yet further aspects, the determination module230can use multiple residuals over a time series in comparison to the crash threshold to determine whether the vehicle100is experiencing an anomaly. The crash threshold is, for example, a defined value that indicates a limit in a lack of correspondence between the predicted and observed responses before taking further action. That is, the crash threshold defines, in one embodiment, an extent to which the predicted response and the observed response are distinct (i.e., fail to correlate) prior to indicating further action should be undertaken. Thus, the crash threshold may indicate a percentage, an actual disparity value, or another metric by which the determination module230can determine correspondence between the responses. Additionally, as used herein, satisfying the crash threshold can include equaling the threshold, exceeding the threshold, and/or meeting criteria of one or more rules/functions as may be defined by a particular implementation. In the instance of a more complex criteria, the determination module230may determine whether the residual(s) satisfy the crash threshold through an analysis involving a statistical test such as a median filter, a norm 1, norm 2, norm infinity function, a thresholding filter, or another suitable analysis for identifying when the residual satisfies the crash threshold and, thus, corresponds with an anomaly.

Consequently, when the determination module230determines that the residual satisfies the crash threshold, then the crash prediction system170has effectively identified an anomaly in the operation of the vehicle100since the predicted response does not adequately correspond with the observed response. In any case, the determination module230compares the residual with the crash threshold to identify whether the extent of the correlation indicates a disparity that corresponds to the anomaly, where the anomaly indicates that the observed response does not correlate with the predicted response. In one or more embodiments, such a determination may be considered sufficient to generate an alert, while in further embodiments, the determination module230undertakes a further analysis to validate the anomaly. Moreover, the anomaly itself may correspond with a crash, or another unexpected event such as a skidding event (e.g., the vehicle100contacting a low friction portion of the roadway and sliding), the presence of debris in the roadway, and so on. Accordingly, the determination module230may further validate the anomaly prior to issuing an alert.

For example, in one embodiment, the determination module230, in one or more aspects, undertakes an analysis of the sensor data from a time subsequent (e.g., spanning 30 seconds thereafter) to the anomaly to validate whether a crash has actually occurred. That is, even though the predicted response and the observed do not necessarily correlate, various aberrations that are not crashes may still cause such anomalies such as a strong jerk of the steering wheel, a quick acceleration, etc. With this being the case, the determination module230uses, in one or more aspects, a set of rules to further validate the anomaly.

By way of example, the set of rules can include various additional operating characteristics of the vehicle100that are considered to confirm the occurrence or at least increase a likelihood of the occurrence of a crash. Thus, in various approaches, the set of rules may include whether one or more safety systems (e.g., airbags, traction control, anti-lock braking, stability control, restraint sensors, etc.) of the vehicle100have been activated, detection of forces exceeding a peak threshold (e.g., IMU sensor detecting lateral acceleration forces exceeding a defined threshold), the vehicle stopping, an occurrence of multiple anomalies (i.e., the system170detecting the residual satisfying the crash threshold) in succession, and so on. In an embodiment where the determination module230further determines correspondence with non-crash events (i.e., skidding, debris, etc.), the rules can define subsequent expected actions such as return to normal operation, peak thresholds in steering to maneuver the vehicle100back to an expected position, and so on.

It should be appreciated the preceding list is provided for purposes of explanation only as an example set of rules and is not intended to be a comprehensive listing of possible rules. Moreover, the determination module230, in one approach, proceeds with generating an alert as subsequently discussed upon identifying at least one of the rules being satisfied. In further aspects, the determination module230follows a policy for determining whether an adequate number of the rules have been satisfied prior to proceeding with generating the alert. For example, in one approach, the determination module230may weigh the rules differently according to a type or associated aspect of the vehicle100that is related to rule. As one example, the determination module230may consider deployment of the airbags as being sufficient to validate the anomaly. As another example, the determination module230may consider the vehicle100stopping in combination with the detection of two successive anomalies as being adequate. The particular arrangement of rules is generally defined according to the policy and may be done on the basis of a particular implementation.

In any case, the determination module230, in response to detecting the anomaly and the one or more rules being satisfied, proceeds with generating an alert. The alert is, for example, an indication by the crash prediction system170that the vehicle has likely crashed. As a general matter, the crash prediction system170generates the alert on the basis of the previously described determinations, and thus provides the alert according to the assertion that the vehicle has crashed with a high likelihood of accuracy. Of course, in an embodiment associated with generating the alert for non-crash events, the determination module230generates the alert to include information associated with the anomaly and further provides the alert in a fashion consistent with the non-crash event (e.g., logs the associated information).

As such, the determination module230may perform various actions as part of providing the alert. For example, in one embodiment, the determination module230provides a confirmation request to a passenger/driver of the vehicle100to confirm the crash. Thus, if the passenger/driver responds affirmatively or does not respond in a defined time period, then the determination module230, in one embodiment, proceeds with additional actions such as communicating the alert to emergency services or other emergency contacts. Of course, in further embodiments, the determination module230may perform additional actions without providing a confirmation message.

In any case, the determination module230, in one or more embodiments, generates the alert by electronically communicating the alert to one or more safety services (e.g., EMS, Fire, police, etc.), logging information and communicating the information to an OEM or other repository for storing crash data, and so on. In this way, the crash prediction system170improves safety of vehicle occupants through more reliable crash detection from which many different mitigating actions may be better implemented.

FIG.4illustrates a flowchart of a method400that is associated with improving crash prediction through correlating an observed response and a predicted response of a vehicle. Method400will be discussed from the perspective of the crash prediction system170ofFIGS.1-2. While method400is discussed in combination with the crash prediction system170, it should be appreciated that the method400is not limited to being implemented within the crash prediction system170but is instead one example of a system that may implement the method400. Of course, while the method is illustrated as a generally serial process, various aspects of the method400can execute in parallel to perform the noted functions.

At410, the crash prediction system170acquires the sensor data250including at least vehicle control inputs. In one embodiment, the crash prediction system170controls the sensor system120to acquire the sensor data250from various sensors within the vehicle100that inform the determination of whether a crash has occurred. For example, in at least one approach, the crash prediction system170acquires the sensor data250about vehicle control inputs (e.g., steering angle, accelerator pedal pressure, brake pedal pressure, transmission gear position, etc.). While pedal pressures and steering wheel positions are generally discussed throughout this disclosure, it should be appreciated that the crash prediction system170can function with manually driven vehicles, semi-autonomous vehicles, or fully autonomous vehicles. Thus, in the instance of a semi or fully autonomous vehicle, the crash prediction system170acquires the sensor data250about automated inputs that may be electronically actuated as opposed to manually driven by pedals and/or steering wheels. However, the general attributes of the information are similar and generally compatible.

In addition to vehicle input controls, the crash prediction system170, in one or more approaches, acquires additional sensor data250including speed, engine rpm, wheel speeds, g-forces, safety system activation controls, and so on. Moreover, the system170controls the sensors to acquire the sensor data250at successive iterations or time steps. Thus, the system170, in one embodiment, iteratively executes the functions discussed at blocks410-460to acquire the sensor data250and provide information therefrom. Furthermore, the system170, in one embodiment, executes one or more of the noted functions in parallel for separate observations in order to maintain updated sensor data250and determinations about the operation of the vehicle100. In one aspect, the crash prediction system170may be generating predicted responses according to current sensor data250while comparing prior predicted responses with observed responses in parallel. Thus, in one approach, the crash prediction system170may execute multiple iterations of the method400in parallel.

At420, the system170selects the response model260from a set of situational models according to the current vehicle control inputs. As previously described, the response model260is, in one embodiment, specific to the particular control inputs that are presently applied to the vehicle100. Thus, prior to predicting the response, the system170selects a model that is trained according to the circumstances under which the vehicle100is presently operating. In any case, all of the models in the set are, for example, machine learning models such as linear or non-linear learning-based regression models.

At430, the system170generates the predicted response for the vehicle100as a function of the selected response model260and according to at least current vehicle inputs. By way of example, in the instance of a longitudinal sub-model of the response model260, the system170uses different aspects of the sensor data250as inputs depending on the selected model. Thus, where the inputs indicate only accelerator pedal pressure, the system170uses the accelerator pressure value, vehicle speed, and engine RPM as electronic inputs to the longitudinal sub-model. In the instance of only brake pedal inputs, the system170uses brake pedal pressure as input to the sub-model. Additionally, in the instance of neither accelerator inputs nor brake pedal inputs, the system170uses the vehicle speed, and engine RPM as electronic inputs to the sub-model. Separately, yaw rate sub-models and lateral acceleration sub-models may use electronic inputs including vehicle speed, wheel speeds, and steering angle. The respective models can then process the noted electronic inputs to generate the predicted response that includes at least yaw rate, lateral acceleration, and longitudinal acceleration.

At440, the system170acquires further sensor data250that corresponds with the observed response. In one embodiment, the system170acquires sensor data embodying the observed response to characterize operating characteristics of the vehicle at a point in time corresponding with the predicted response (e.g., 0.5 seconds beyond a time corresponding to data used to perform the predictions). As previously described, the system170generates the observed response to include a component-to-component correspondence with the predicted response. Thus, in the present example, the observed response includes the observed yaw rate, the observed lateral acceleration, and the observed longitudinal acceleration.

At450, the system170computes a residual indicating an extent of correlation between the predicted response and the observed response. In one embodiment, the system170generates the residual by comparing the predicted response with the observed response. As previously noted, the precise form of this comparison may vary according to the implementation but is generally intended to determine when the predicted response correlates with the observed response (i.e., when the responses are similar). As such, the system170may implement the comparison through a simple differencing operation between separate corresponding components or through a more complex heuristic that distills the separate correlations/disparities into a single value. In any case, the residual reflects how closely the two responses correspond.

At460, the crash prediction system170determines whether the residual satisfies the crash threshold. In one embodiment, the crash threshold defines a value of the residual beyond which the responses are not considered to correlate sufficiently, and, thus, indicate an instance in which an anomaly exists in the behavior of the vehicle100. The crash threshold itself may be a percentage or other value that corresponds with the residual and identifies a limit on the failure of correlation between the responses. Thus, the crash prediction system170may determine whether the residual satisfies the crash threshold through a basic inequality comparison and/or using another heuristic. For example, in one approach, the crash prediction system170applies a statistical test such as a median filter, a norm 1, norm 2, norm infinity function, a thresholding filter, or another suitable analysis for identifying when the residual satisfies the crash threshold and, thus, corresponds with an anomaly. Additionally, it should be noted that while satisfying the crash threshold is generally discussed as being associated with a crash, in further aspects, the residual value may indicate other anomalies such as a presence of debris on the road, low friction of the road due to oil, ice, water, etc. Such further anomalies may be additionally confirmed by rules as further discussed at470(e.g., by comparing the braking/acceleration/turning performance with a baseline, if the residuals are consistently in one direction).

At470, when the system170determines that the residual satisfies the crash threshold, the system170proceeds to analyze the sensor data250about operating characteristics of the vehicle100during a time subsequent to the observed response and according to a set of rules. In one embodiment, the system undertakes this additional analysis to validate whether the vehicle100has crashed (i.e., whether the anomaly actually corresponds with a crash). Moreover, as noted above, the set of rules specify one or more aspects about the operating characteristics of the vehicle100that correspond with the vehicle100crashing. The set of rules can include various conditions associated with operation of the vehicle100such as dynamics (i.e., the vehicle stops, strong jerking motions, etc.), identification of multiple successive anomalies, and so on.

At480, the crash prediction system170determines whether one or more rules have been satisfied. In one embodiment, as previously explained, the crash prediction170determines which combinations of rules being satisfied are sufficient for validating the anomaly as a crash. Thus, depending on the particular implementation, a policy may indicate one or more particular rules that are to be satisfied (i.e. conditions met) in order to validate the anomaly and proceed with generating the alert at490. That is, in one approach, depending on how the policy weighs different rules, a single rule and/or a combination of rules may need to be met in order to satisfy the policy and proceed.

At490, the crash prediction system170provides an alert indicating the vehicle100has likely crashed. In one embodiment, the crash prediction system170provides the alert by electronically communicating the alert to one or more third-party services. The third-party services can include emergency response services (e.g., EMS, fire, road-side assistance, etc.), internal systems of the vehicle100(e.g., to cease operation thereby preventing fire or further damage), information logging services (e.g., block-box logging to identify information associated with the crash), emergency contacts, nearby vehicles via V2V or another communication network (for avoiding an area of the crash), and so on. In this way, the disclosed method400functions to improve the detection of crash events from which additional assisting services may be more reliably dispatched and/or otherwise utilized.

FIG.5illustrates an example of how the crash prediction system170may predict responses of the vehicle100and determine the occurrence of a crash. As shown inFIG.5, a roadway500includes the vehicle100traveling in a center lane next to an adjacent vehicle510. Thus, at a first instance in time, the vehicle100acquires sensor data of the vehicle100and generates a predicted response530that corresponds with an expected future position of the vehicle100shown by vehicle520. However, when the vehicle510unexpectedly travels toward the vehicle100, the vehicle100steers abruptly away from the vehicle510resulting in an observed response550associated with a different trajectory. As shown, the vehicle540represents the vehicle100at a subsequent time step. Thus, the crash prediction system170compares the predicted response530and the observed response550and identifies that the responses do not correlate. Thus, depending on subsequent behaviors of the vehicle100at a position540(e.g., collision with a curb, etc.), the crash prediction system170may indicate the occurrence of a crash.

FIG.6illustrates another example of how the crash prediction system170may predict responses of the vehicle100and determine the occurrence of a crash. As shown inFIG.6, a roadway600includes the vehicle100traveling in a center lane adjacent to a row of vehicles610. Unseen by the vehicle100is a pedestrian620obscured by the vehicles610. Thus, the crash prediction system170generates a predicted response630according to current sensor data that does not account for the pedestrian620, and that would result in an expected position640for the vehicle100. However, as the vehicle100proceeds at a subsequent time step, the pedestrian proceeds along a trajectory into the roadway600causing the vehicle100to quickly steer away from the pedestrian620resulting in an observed response650and a future position660that are quite distinct from the predicted response630. Thus, the crash prediction system170compares the predicted response630and the observed response650and identifies that the responses do not correlate. Thus, depending on subsequent behaviors of the vehicle100at the position660and beyond (e.g., collision with another vehicle, curb, wall, ditch, etc.), the crash prediction system170may indicate the occurrence of a crash.

FIG.7illustrates a further example of how the crash prediction system170may predict responses of the vehicle100and determine the occurrence of a crash. As shown inFIG.7, a roadway700includes a leftward turning corner and includes the vehicle100traveling along the roadway700. Further consider that the roadway may be slippery due to weather conditions such as rain or ice. Thus, at a first instance in time, the vehicle100acquires sensor data and generates a predicted response710that corresponds with a future position720for the vehicle100along the roadway700.

However, when the vehicle100unexpectedly encounters a slippery section of the roadway700at the curve, an observed response730associated with the vehicle100shows how the vehicle100proceeds off of the curve to a position shown by740. Thus, the crash prediction system170compares the predicted response710and the observed response730and identifies that the responses do not correlate. Thus, depending on subsequent behaviors of the vehicle100at a position740(e.g., vehicle stops off of the roadway) the crash prediction system170may indicate the occurrence of a crash. In this way, the crash prediction system170leverages the available sensor data to identify when the vehicle100has crashed and may then perform additional actions to improve the safety of the passengers such as dispatching emergency services, logging data to improve operation of the vehicle100under similar conditions subsequently, and so on.

FIG.1will now be discussed in full detail as an example environment within which the system and methods disclosed herein may operate. In some instances, the vehicle100is configured to switch selectively between an autonomous mode, one or more semi-autonomous operational modes, and/or a manual mode. Such switching can be implemented in a suitable manner, now known or later developed. “Manual mode” means that all of or a majority of the navigation and/or maneuvering of the vehicle is performed according to inputs received from a user (e.g., human driver). In one or more arrangements, the vehicle100can be a conventional vehicle that is configured to operate in only a manual mode.

In one or more arrangements, the map data116can include one or more terrain maps117. The terrain map(s)117can include information about the ground, terrain, roads, surfaces, and/or other features of one or more geographic areas. The terrain map(s)117can include elevation data in the one or more geographic areas. The map data116can be high quality and/or highly detailed. The terrain map(s)117can define one or more ground surfaces, which can include paved roads, unpaved roads, land, and other things that define a ground surface.

As an example, in one or more arrangements, the sensor system120can include one or more radar sensors123, one or more LIDAR sensors124, one or more sonar sensors125, and/or one or more cameras126. In one or more arrangements, the one or more cameras126can be high dynamic range (HDR) cameras or infrared (IR) cameras.

The vehicle100can include an input system130. An “input system” includes any device, component, system, element or arrangement or groups thereof that enable information/data to be entered into a machine. The input system130can receive an input from a vehicle passenger (e.g., a driver or a passenger). The vehicle100can include an output system135. An “output system” includes any device, component, or arrangement or groups thereof that enable information/data to be presented to a vehicle passenger (e.g., a person, a vehicle passenger, etc.).

The processor(s)110, the crash prediction system170, and/or the autonomous driving module(s)160may be operable to control the navigation and/or maneuvering of the vehicle100by controlling one or more of the vehicle systems140and/or components thereof. For instance, when operating in an autonomous mode, the processor(s)110, the crash prediction system170, and/or the autonomous driving module(s)160can control the direction and/or speed of the vehicle100. The processor(s)110, the crash prediction system170, and/or the autonomous driving module(s)160can cause the vehicle100to accelerate (e.g., by increasing the supply of fuel provided to the engine), decelerate (e.g., by decreasing the supply of fuel to the engine and/or by applying brakes) and/or change direction (e.g., by turning the front two wheels). As used herein, “cause” or “causing” means to make, force, compel, direct, command, instruct, and/or enable an event or action to occur or at least be in a state where such event or action may occur, either in a direct or indirect manner.