Anomaly detection systems and methods

Systems and method are provided for detecting an anomaly of a sensor of a vehicle. In one embodiment, a method includes: storing a plurality of sensor correlation groups based on vehicle dynamics; processing a subset of signals based on the sensor correlation groups to determine when an anomaly exists; processing the subset of signals based on the sensor correlation group to determine which sensor of the sensor correlation group is anomalous; and generating notification data based on the sensor of the correlation group that is anomalous.

The present disclosure generally relates to vehicles, and more particularly relates to systems and methods for detecting anomalies within a vehicle.

An autonomous or semi-autonomous vehicle is a vehicle that is capable of sensing its environment and navigating with little or no user input. An autonomous vehicle senses its environment using sensing devices such as inertial measurement units, radar, LIDAR, image sensors, and the like. The autonomous vehicle system further uses information from global positioning systems (GPS) technology, navigation systems, vehicle-to-vehicle communication, vehicle-to-infrastructure technology, and/or drive-by-wire systems to navigate the vehicle.

In addition, the autonomous or semi-autonomous vehicles rely on sensor data from sensors that monitor observable conditions of the vehicle such as, but not limited to, speed sensors, position sensors, yaw rate sensors, inertial sensors, etc. In some instances, one or more of the sensors can become faulty. Faults in a sensor and/or faults or malicious attacks of the corresponding sensor data can cause the controlling software to perceive the environment and/or the vehicle functions incorrectly and thus, lead the system to make poor decisions. Accordingly, it is desirable to provide improved methods and systems for detecting anomalies of sensors of the vehicle. It is further desirable to provide methods and systems for detecting anomalies of sensors of other vehicles. Furthermore, other desirable features and characteristics of the present invention will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background.

SUMMARY

Systems and method are provided for detecting an anomaly of a sensor of a vehicle. In one embodiment, a method includes: storing a plurality of sensor correlation groups based on vehicle dynamics; processing a subset of signals based on the sensor correlation groups to determine when an anomaly exists; processing the subset of signals based on the sensor correlation group to determine which sensor of the sensor correlation group is anomalous; and generating notification data based on the sensor of the correlation group that is anomalous.

In various embodiments, the plurality of sensor correlation groups includes a position group, a speed group, an acceleration group, a heading group, and a yaw rate group. In various embodiments, each of the plurality of sensor correlation groups includes one or more sensors correlated to the group based on vehicle dynamics. In various embodiments, the subset of signals includes a position signal, a speed signal, an acceleration signal, a heading signal, and a yaw rate signal.

In various embodiments, the processing the subset of signals to determine when the anomaly exists includes computing a mean absolute error (MAE) based on the plurality of correlation groups.

In various embodiments, the processing the subset of signals to determine when the anomaly exists is based on a defined batch size.

In various embodiments, the processing the subset of signals to determine which sensor of the sensor correlation group is anomalous is based on a depth first search method and the plurality of sensor correlation groups.

In various embodiments, the processing the subset of signals to determine which sensor of the sensor correlation group is anomalous is based on a computed mean of a predicted condition vector associated with a sensor correlation group of the plurality of correlation groups.

In various embodiments, the method includes training a plurality of thresholds based on anomalous signal data, and wherein the processing the subset of signals to determine which sensor of the sensor correlation group is anomalous is based on the plurality of trained thresholds.

In various embodiments, the method includes training a plurality of thresholds based on anomalous signal data, and wherein the processing the subset of signals to determine when a sensor of the sensor correlation group is anomalous is based on the plurality of trained thresholds.

In another embodiment, a computer implemented system is provided. The computer implemented system includes an anomaly detection module that includes one or more processors configured by programming instructions encoded in non-transitory computer readable media, the anomaly detection module configured to: store a plurality of sensor correlation groups based on vehicle dynamics; process a subset of signals based on the sensor correlation groups to determine when an anomaly exists; process the subset of signals based on the sensor correlation group to determine which sensor of the sensor correlation group is anomalous; and generate notification data based on the sensor of the correlation group that is anomalous.

In various embodiments, the plurality of sensor correlation groups includes a position group, a speed group, an acceleration group, a heading group, and a yaw rate group. In various embodiments, each of the plurality of sensor correlation groups includes one or more sensors correlated to the group based on vehicle dynamics.

In various embodiments, the subset of signals includes a position signal, a speed signal, an acceleration signal, a heading signal, and a yaw rate signal.

In various embodiments, the anomaly detection module is configured to process the subset of signals to determine when the anomaly exists by computing a mean absolute error (MAE) based on the plurality of correlation groups.

In various embodiments, the anomaly detection module is configured to process the subset of signals to determine when the anomaly exists based on a defined batch size.

In various embodiments, the anomaly detection module is configured to process the subset of signals to determine which sensor of the sensor correlation group is anomalous based on a depth first search method and the plurality of sensor correlation groups.

In various embodiments, the anomaly detection module is configured to process the subset of signals to determine which sensor of the sensor correlation group is anomalous based on a computed mean of a predicted condition vector associated with a sensor correlation group of the plurality of correlation groups.

In various embodiments, wherein the anomaly detection module is configured to train a plurality of thresholds based on anomalous signal data, and process the subset of signals to determine which sensor of the sensor correlation group is anomalous based on the plurality of trained thresholds.

In various embodiments, the anomaly detection module is configured to train a plurality of thresholds based on anomalous signal data, and process the subset of signals to determine when a sensor of the sensor correlation group is anomalous based on the plurality of trained thresholds.

DETAILED DESCRIPTION

With reference toFIG.1, an anomaly detection system shown generally at100is associated with a vehicle10in accordance with various embodiments. In general, the anomaly detection system100receives and processes signal data in order to detect data-centric anomalies in real-time in both the vehicle10(the ego vehicle) and other vehicles (communicating with the ego vehicle) using a subset of signals from Vehicle to Everything (V2X) messages such as, but not limited to, basic emergency and safety messages as will be discussed in more detail below. In various embodiments, the anomaly detection system100traces the origin of a detected anomaly to a specific signal or set of signals. In various embodiments, the anomaly detection system100verifies the correctness of its own signal data as well as protects the vehicle10from anomalous/misbehaving data from other vehicles.

As depicted in the example ofFIG.1, the vehicle10is an automobile and generally includes a chassis12, a body14, front wheels16, and rear wheels18. The body14is arranged on the chassis12and substantially encloses components of the vehicle10. The body14and the chassis12may jointly form a frame. The wheels16-18are each rotationally coupled to the chassis12near a respective corner of the body14.

As shown, the vehicle10generally includes a propulsion system20, a transmission system22, a steering system24, a brake system26, a sensor system28, an actuator system30, at least one data storage device32, at least one controller34, and a communication system36. The propulsion system20, in various embodiments, includes an internal combustion engine, an electric machine, such as a traction motor powered by one or more batteries, alone (e.g., as a pure electric vehicle) or in combination with an internal combustion engine, and/or a fuel cell propulsion system (e.g., as a hybrid electric vehicle).

The transmission system22is configured to transmit power from the propulsion system20to the vehicle wheels16-18according to selectable speed ratios. According to various embodiments, the transmission system22may include a step-ratio automatic transmission, a continuously-variable transmission, or other appropriate transmission. The brake system26is configured to provide braking torque to the vehicle wheels16-18. The brake system26may, in various embodiments, include friction brakes, brake by wire, a regenerative braking system such as an electric machine, and/or other appropriate braking systems. The steering system24influences a position of the of the vehicle wheels16-18.

The sensor system28includes one or more sensing devices40a-40nthat sense observable conditions of the exterior environment and/or the interior environment of the vehicle10. The sensing devices40a-40ncan include, but are not limited to, radars, lidars, global positioning systems, optical cameras, thermal cameras, ultrasonic sensors, inertial measurement units, and/or other sensors. In various embodiments, the sensor system28further includes one or more sensing devices41a-41nthat sense observable conditions of one or more vehicle components.

The actuator system30includes one or more actuator devices42a-42nthat control one or more vehicle features such as, but not limited to, the propulsion system20, the transmission system22, the steering system24, and the brake system26. In various embodiments, the vehicle features can further include interior and/or exterior vehicle features such as, but are not limited to, doors, a trunk, and cabin features such as air, music, lighting, etc. (not numbered).

The communication system36is configured to wirelessly communicate information to and from other entities48, such as but not limited to, other vehicles (“V2V” communication) infrastructure (“V2I” communication), everything (“V2X” communication), remote systems, charging stations, and/or personal devices (described in more detail with regard toFIG.2). In an exemplary embodiment, the communication system36is a wireless communication system configured to communicate via a wireless local area network (WLAN) using IEEE 802.11 standards or by using cellular data communication. However, additional or alternate communication methods, such as a dedicated short-range communications (DSRC) channel or cellular V2X (C-V2X), are also considered within the scope of the present disclosure. DSRC channels refer to one-way or two-way short-range to medium-range wireless communication channels specifically designed for automotive use and a corresponding set of protocols and standards.

The data storage device32stores data for use in controlling the autonomous vehicle10. In various embodiments, the data storage device32stores defined maps of the navigable environment. In various embodiments, the defined maps may be predefined by and obtained from a remote system (described in further detail with regard toFIG.2). For example, the defined maps may be assembled by the remote system and communicated to the autonomous vehicle10(wirelessly and/or in a wired manner) and stored in the data storage device32. Route information may also be stored within data storage device32—i.e., a set of road segments (associated geographically with one or more of the defined maps) that together define a route that the user may take to travel from a start location (e.g., the user's current location) to a target location. As can be appreciated, the data storage device32may be part of the controller34, separate from the controller34, or part of the controller34and part of a separate system.

The instructions of the controller34may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions. The instructions, when executed by the processor44, receive and process signals from the sensor system28, perform logic, calculations, methods and/or algorithms for automatically controlling the components of the vehicle10, and generate control signals to the actuator system30to automatically control the components of the vehicle10based on the logic, calculations, methods, and/or algorithms. Although only one controller34is shown inFIG.1, embodiments of the vehicle10can include any number of controllers34that communicate over any suitable communication medium or a combination of communication mediums and that cooperate to process the sensor signals, perform logic, calculations, methods, and/or algorithms, and generate control signals to control features of the vehicle10. As mentioned briefly above, all or part of the anomaly detection system100ofFIG.1is included within the controller34.

With reference now toFIG.2, where an operating environment of the anomaly detection system100is shown generally at50that includes a remote transportation system52that is associated with and communicates with one or more vehicles10a-10nas described with regard toFIG.1. In various embodiments, the operating environment50further includes one or more user devices54that communicate with the vehicles10a-10nand/or the remote transportation system52via a communication network56.

The communication network56supports communication as needed between devices, systems, and components supported by the operating environment50(e.g., via tangible communication links and/or wireless communication links). For example, the communication network56can include a wireless carrier system60such as a cellular telephone system that includes a plurality of cell towers (not shown), one or more mobile switching centers (MSCs) (not shown), as well as any other networking components required to connect the wireless carrier system60with a land communications system. Each cell tower includes sending and receiving antennas and a base station, with the base stations from different cell towers being connected to the MSC either directly or via intermediary equipment such as a base station controller. The wireless carrier system60can implement any suitable communications technology, including for example, digital technologies such as CDMA (e.g., CDMA2000), LTE (e.g., 4G LTE or 5G LTE), GSM/GPRS, or other current or emerging wireless technologies. Other cell tower/base station/MSC arrangements are possible and could be used with the wireless carrier system60. For example, the base station and cell tower could be co-located at the same site or they could be remotely located from one another, each base station could be responsible for a single cell tower or a single base station could service various cell towers, or various base stations could be coupled to a single MSC, to name but a few of the possible arrangements.

Apart from including the wireless carrier system60, a second wireless carrier system in the form of a satellite communication system64can be included to provide uni-directional or bi-directional communication with the vehicles10a-10n. This can be done using one or more communication satellites (not shown) and an uplink transmitting station (not shown). Uni-directional communication can include, for example, satellite radio services, wherein programming content (news, music, etc.) is received by the transmitting station, packaged for upload, and then sent to the satellite, which broadcasts the programming to subscribers. Bi-directional communication can include, for example, satellite telephony services using the satellite to relay telephone communications between the vehicle10and the station. The satellite telephony can be utilized either in addition to or in lieu of the wireless carrier system60.

A land communication system62may further be included that is a conventional land-based telecommunications network connected to one or more landline telephones and connects the wireless carrier system60to the remote transportation system52. For example, the land communication system62may include a public switched telephone network (PSTN) such as that used to provide hardwired telephony, packet-switched data communications, and the Internet infrastructure. One or more segments of the land communication system62can be implemented through the use of a standard wired network, a fiber or other optical network, a cable network, power lines, other wireless networks such as wireless local area networks (WLANs), or networks providing broadband wireless access (BWA), or any combination thereof. Furthermore, the remote transportation system52need not be connected via the land communication system62, but can include wireless telephony equipment so that it can communicate directly with a wireless network, such as the wireless carrier system60.

Although only one user device54is shown inFIG.2, embodiments of the operating environment50can support any number of user devices54, including multiple user devices54owned, operated, or otherwise used by one person. Each user device54supported by the operating environment50may be implemented using any suitable hardware platform. In this regard, the user device54can be realized in any common form factor including, but not limited to: a desktop computer; a mobile computer (e.g., a tablet computer, a laptop computer, or a netbook computer); a smartphone; a video game device; a digital media player; a piece of home entertainment equipment; a digital camera or video camera; a wearable computing device (e.g., smart watch, smart glasses, smart clothing); or the like. Each user device54supported by the operating environment50is realized as a computer-implemented or computer-based device having the hardware, software, firmware, and/or processing logic needed to carry out the various techniques and methodologies described herein. For example, the user device54includes a microprocessor in the form of a programmable device that includes one or more instructions stored in an internal memory structure and applied to receive binary input to create binary output. In some embodiments, the user device54includes a GPS module capable of receiving GPS satellite signals and generating GPS coordinates based on those signals. In other embodiments, the user device54includes cellular communications functionality such that the device carries out voice and/or data communications over the communication network56using one or more cellular communications protocols, as are discussed herein. In various embodiments, the user device54includes a visual display, such as a touch-screen graphical display, or other display.

The remote transportation system52includes one or more backend server systems, which may be cloud-based, network-based, or resident at the particular campus or geographical location serviced by the remote transportation system52. The remote transportation system52can be manned by a live advisor, or an automated advisor, or a combination of both. The remote transportation system52can communicate with the user devices54and/or the vehicles10a-10nto schedule rides, dispatch vehicles10a-10n, communicate information, and the like as will be discussed in more detail below.

As can be appreciated, the subject matter disclosed herein provides certain enhanced features and functionality to what may be considered as a standard or baseline vehicle10and/or remote transportation system52. To this end, a vehicle and a remote transportation system can be modified, enhanced, or otherwise supplemented to provide the additional features of the anomaly detection system100disclosed herein.

As shown in more detail with regard toFIG.3and with continued reference toFIG.1, the anomaly detection system100may be implemented as one or more modules configured to perform one or more methods by way of, for example, a processor. As can be appreciated, the modules shown inFIG.3can be combined and/or further partitioned in order to perform the functions or methods described herein. Furthermore, inputs to the modules may be received from the sensor system28, received from other control modules (not shown) associated with the vehicle10, received from the communication system36, and/or determined/modeled by other sub-modules (not shown) within the controller34ofFIG.1. Furthermore, the inputs might also be subjected to preprocessing, such as sub-sampling, noise-reduction, normalization, feature-extraction, missing data reduction, and the like. In various embodiments, the anomaly detection system100includes a training module102, an anomaly detection module104, a source identification module106, a groups datastore108, and a parameters datastore110.

In various embodiments, the groups datastore108stores groups data112relating to sensor pairs. For example, vehicle dynamics can be expressed with fundamental physics equations where sensor pairs are inherently cross-correlated. Correlation groups are created based on these physics' equations. For example, given the following relationships:

five correlation groups can be established including position, speed, acceleration, heading, and yaw rate. These groups are pre-established and stored as groups data112in the groups datastore108.

In various embodiments, the anomaly detection system100is configured to operate in two modes, such as, but not limited to, an offline mode and an online mode. For example, during design time and before manufacturing, the anomaly detection system100is operated in a training mode where parameters are determined and stored in the parameters datastore110for use by the anomaly detection module104, and the source identification module106. In another example, during operation of the vehicle10, the anomaly detection system100is operated in an online mode where signal data is continuously processed based on the stored parameters from the parameters datastore110.

In various embodiments, the training module102operates in the offline mode and receives anomaly data116, signal data118, and the groups data112. The signal data118can include non-anomalous data. Based on the received data, the training module102trains the parameters that are used in the online mode and stores the parameters as parameters data114in the parameters datastore110. In various embodiments, the parameters include a threshold for each of the sensor groups. The training module102determines the threshold by computing a minimal mean absolute error (MAE) from the signal data118yielding the threshold θmeanfor each respective correlation group. For example, the signal data118may include multiple trips which are indicated by Vehicle ID and Trip ID. For each trip, the five correlation groups are applied, and an estimated signal is computed. The estimated signal is subtracted from the received signal to obtain a threshold or the trip. If signal data118has N trips, then then N thresholds can be computed. The mean of all these N thresholds yields resulting θmean.

Thereafter, the training module102computes a Fallout/False Positive Rate (FPR) for each correlation group. The false positive rate is computed as the ratio between the number of negative events wrongly categorized as positive and the total number of actual negative events. For example, when no (or very few) anomaly is injected on a specific sensor, no anomalies are detected and the computed FPR is small. The training module stores the computed FPRs and the thresholds as the parameters data114in the parameters datastore110.

In various embodiments, the anomaly detection module104operates in the online mode and receives signal data120, the groups data112, and the parameters data114. The signal data120is generated as a result of vehicle operation and may be communicated in messages within the vehicle10or in messages sent from other vehicles. Thus, the anomalies can be detected for sensors within the vehicle or for sensors of other vehicles, in various embodiments.

The anomaly detection module104evaluates the signal data120based on the groups identified by the groups data112to determine if an anomaly in one of the signals exists. For example, in various embodiments, the MAE is computed for all five correlation groups and compared to the threshold θ indicated by the parameters data114. When the computed MAE is larger than the threshold θ, the sensor group is marked as anomalous. When the computed MAE is less than the threshold θ, the sensor group is marked as non-anomalous. After W samples, the samples are smoothed by a rolling mean filter of window size W and then the MAE is recalculated and compared again to the threshold to update the labels (anomalous or non-anomalous). To mitigate measurement error and improve performance, a batch size B is used for the evaluations. For example, when only 0.1*B predictions in the last B samples are anomalous, all predictions are marked as non-anomalous.

In various embodiments, the source identification module106receives the anomaly detection data122, the parameters data114, and the groups data112. When the anomaly detection data122indicates an anomaly is found, the source identification module106processes the received data to determine a source of the of the detected anomaly. For example, the source identification module106computes for each sensor group, a mean of the predicted condition vectorPC. When thePCis smaller than the trained threshold (FPR) indicated by the parameters data114, then the sensor is not anomalous.

When thePCis larger than the trained threshold, then the sensor is anomalous and the source detection data124is updated to indicate an anomaly is detected at the sensor relating to the sensor group.

In various embodiments, the source identification module106processes each sensor group by natural order of iteration through the correlation groups. For example, the source identification module106processes each sensor group using a depth first search (DFS) traversal of a three.

The traversal will end when an anomaly source is determined. Thus, only one anomaly source can be detected. When the entire tree is traversed because the computed FPR is always lower than the threshold, an anomaly is not detected on any sensor and the source detection data124is updated to indicate no anomaly detected.

Referring nowFIGS.5and6, and with continued reference toFIGS.1-4, a flowchart illustrates methods200,400that can be performed by the anomaly detection system100ofFIGS.1-3in accordance with the present disclosure. As can be appreciated in light of the disclosure, the order of operation within the methods is not limited to the sequential execution as illustrated inFIGS.5and6but may be performed in one or more varying orders as applicable and in accordance with the present disclosure. In various embodiments, the methods200,400can be scheduled to run based on one or more predetermined events, and/or can run continuously during operation of the vehicle10or the remote transportation system52.

In various embodiments, the method200may be performed to detect an anomaly. In one example, the method200begin at205. The values are estimated based on the signal data120and, for example, equations above for each of the correlation groups at207. The MAE is computed from all five correlation groups at210. The MAE is then compared to the threshold θ indicated by the parameters data114at220. When the computed MAE is larger than the threshold θ at220, the sensor group is marked as anomalous at230and the anomaly detection data122is updated. When the computed MAE is less than the threshold θ at220, the sensor group is marked as non-anomalous at240.

After W samples have been evaluated at250, a prediction is updated with a smoothed estimated signal at260. Thereafter, the prediction count is evaluated at270. If the prediction count is greater than the defined batch size B at270, the batch is evaluated at280. For example, when only a defined percentage of the predictions in the last B samples are anomalous at280, all predictions in the batch are marked as non-anomalous at290. Thereafter, the method continues with monitoring the next samples at210.

In various embodiments, the method400may be performed to detect a source of the anomaly once an anomaly has been detected. In one example, the method400begin at405. It is determined that an anomaly was detected at410. A sensor group is selected at420for example using the DFS method as discussed above at420. A mean of the predicted condition vectorPCis computed for the sensor group at430and evaluated at440. When thePCis larger than the threshold at440, the sensor IS anomalous and the source detection data124is updated to indicate an anomaly at460. Thereafter, the method may end at470.

When thePCis smaller than the threshold (FPR) at440, the sensor IS NOT anomalous at450. The method continues with selecting the next sensor group at420. If all sensor groups have been processed at420and no anomalies have been found, the method may end at470.