Driving abnormality detection

A system for detecting abnormal driver behavior includes an input interface and a processor. The input interface is for receiving a sensor data of a vehicle. The processor is for determining a driving behavior based at least in part on the sensor data of the vehicle; determining whether the driving behavior is abnormal; and, in the event that the driving behavior is abnormal, indicating an abnormal driver behavior.

BACKGROUND OF THE INVENTION

Modern vehicles (e.g., airplanes, boats, trains, cars, trucks, etc.) can include a vehicle event recorder in order to better understand the risks and inefficiencies associated with vehicles and their drivers. A vehicle event recorder typically includes a set of sensors, e.g., video recorders, audio recorders, accelerometers, gyroscopes, vehicle state sensors, GPS (global positioning system), etc., that report data, which is used to determine the occurrence of these risks and inefficiencies by recording anomalous events. Sensor data can then be transmitted to an external reviewing system. Anomalous event types include accident anomalous events, maneuver anomalous events, location anomalous events, proximity anomalous events, vehicle malfunction anomalous events, driver behavior anomalous events, or any other anomalous event types. One class of problems leading to risks are atypical driver behaviors.

DETAILED DESCRIPTION

A system for detecting abnormal driver behavior comprises an input interface for receiving a sensor data of a vehicle, and a processor for determining a driving behavior based at least in part on the sensor data of the vehicle, determining whether the driving behavior is abnormal, and in the event that the driving behavior is abnormal, indicating an abnormal driver behavior. In some embodiments, the system additionally comprises a memory coupled to the processor and configured to provide the processor with instructions.

In some embodiments, a system for determining abnormal driver behavior comprises a vehicle event recorder comprising a processor and a memory. The vehicle event recorder is coupled to a set of sensors (e.g., audio sensors, video sensors, accelerometers, gyroscopes, global positioning system sensors, vehicle state sensors, etc.) for recording vehicle data. The vehicle event recorder determines a set of maneuver profiles (e.g., a braking profile, a turning profile, an acceleration profile, etc.) from the data and compares the maneuver profiles with a set of historical maneuver profiles. In some embodiments, the set of historical maneuver profiles comprises a set of historical maneuver profiles for the driver (e.g., the historical maneuver profiles comprise a record of how the driver typically drives). A cumulative abnormality is determined comprising the cumulative deviation of the maneuver profiles from the historical maneuver profiles (e.g., how much the current driver behavior deviates from the typical driver behavior). In the event it is determined that the cumulative abnormality is greater than a threshold, an abnormal driver behavior is indicated.

FIG. 1is a block diagram illustrating an embodiment of a system including a vehicle event recorder. Vehicle event recorder102comprises a vehicle event recorder mounted in a vehicle (e.g., a car or truck). In some embodiments, vehicle event recorder102includes or is in communication with a set of sensors—for example, video recorders, audio recorders, accelerometers, gyroscopes, vehicle state sensors, proximity sensors, a global positioning system (e.g., GPS), outdoor temperature sensors, moisture sensors, laser line tracker sensors, or any other appropriate sensors. In various embodiments, vehicle event recorder102includes a camera that faces outside of a vehicle, which in conjunction with processing, that enables vehicle event recorder102to estimate lane marker positions, estimate other vehicle positions, determine lane departures, determine lane change frequencies, determine headway to other vehicles, or any appropriate driving data. In some embodiments, vehicle event recorder102includes a camera that faces inside of a vehicle, which in conjunction with processing, that enables vehicle event recorder102to estimate a degree of smiling and/or frowning. In various embodiments, vehicle state sensors comprise a speedometer, an accelerator pedal sensor, a brake pedal sensor, an engine revolutions per minute (e.g., RPM) sensor, an engine temperature sensor, a headlight sensor, an airbag deployment sensor, driver and passenger seat weight sensors, an anti-locking brake sensor, an engine exhaust sensor, a gear position sensor, a cabin equipment operation sensor, or any other appropriate vehicle state sensors. In some embodiments, vehicle event recorder102comprises a system for processing sensor data and detecting events. In some embodiments, vehicle event recorder102comprises map data. In some embodiments, vehicle event recorder102comprises a system for detecting risky behavior. In various embodiments, vehicle event recorder102is mounted on vehicle106in one of the following locations: the chassis, the front grill, the dashboard, the rear-view mirror, or any other appropriate location. In some embodiments, vehicle event recorder102comprises multiple units mounted in different locations in vehicle106. In some embodiments, vehicle event recorder102comprises a communications system for communicating with network100. In some embodiments, vehicle event recorder102comprises a processor and a memory. In various embodiments, network100comprises a wireless network, a wired network, a cellular network, a Code Division Multiple Access (CDMA) network, a Global System for Mobile Communication (GSM) network, a Long-Term Evolution (LTE) network, a Universal Mobile Telecommunications System (UMTS) network, a Worldwide Interoperability for Microwave Access (WiMAX) network, a Dedicated Short-Range Communications (DSRC) network, a local area network, a wide area network, the Internet, or any other appropriate network. In some embodiments, network100comprises multiple networks, changing over time and location. In some embodiments, different networks comprising network100comprise different bandwidth cost (e.g., a wired network has a very low cost, a wireless Ethernet connection has a moderate cost, a cellular data network has a high cost). In some embodiments, network100has a different cost at different times (e.g., a higher cost during the day and a lower cost at night). Vehicle event recorder102communicates with vehicle data server104via network100. Vehicle event recorder102is mounted to vehicle106. In various embodiments, vehicle106comprises a car, a truck, a commercial vehicle, or any other appropriate vehicle. Vehicle data server104comprises a vehicle data server for collecting events and risky behavior detected by vehicle event recorder102. In some embodiments, vehicle data server104comprises a system for collecting data from multiple vehicle event recorders. In some embodiments, vehicle data server104comprises a system for analyzing vehicle event recorder data. In some embodiments, vehicle data server104comprises a system for displaying vehicle event recorder data. In some embodiments, vehicle data server104is located at a home station (e.g., a shipping company office, a taxi dispatcher, a truck depot, etc.). In various embodiments, vehicle data server104is located at a colocation center (e.g., a center where equipment, space, and bandwidth are available for rental), at a cloud service provider, or any at other appropriate location. In some embodiments, events recorded by vehicle event recorder102are downloaded to vehicle data server104when vehicle106arrives at the home station. In some embodiments, vehicle data server104is located at a remote location. In some embodiments, events recorded by vehicle event recorder102are downloaded to vehicle data server104wirelessly. In some embodiments, a subset of events recorded by vehicle event recorder102is downloaded to vehicle data server104wirelessly. In some embodiments, vehicle event recorder102comprises a system for detecting abnormal driving.

FIG. 2is a block diagram illustrating an embodiment of a vehicle event recorder. In some embodiments, vehicle event recorder200ofFIG. 2comprises vehicle event recorder102ofFIG. 1. In the example shown, vehicle event recorder200comprises processor202. Processor202comprises a processor for controlling the operations of vehicle event recorder200, for reading and writing information on data storage204, for communicating via wireless communications interface206, and for reading data via sensor interface208. In various embodiments, processor202comprises a processor for determining a vehicle characterization, determining a vehicle identifier, determining a maintenance item, or for any other appropriate purpose. Data storage204comprises a data storage (e.g., a random access memory (RAM), a read only memory (ROM), a nonvolatile memory, a flash memory, a hard disk, or any other appropriate data storage). In various embodiments, data storage204comprises a data storage for storing instructions for processor202, vehicle event recorder data, vehicle event data, sensor data, video data, driver scores, or any other appropriate data. In various embodiments, communications interfaces206comprises one or more of a GSM interface, a CDMA interface, a LTE interface, a WiFi™ interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a Bluetooth™ interface, an Internet interface, or any other appropriate interface. Sensor interface208comprises an interface to one or more vehicle event recorder sensors. In various embodiments, vehicle event recorder sensors comprise an exterior video camera, an exterior still camera, an interior video camera, an interior still camera, a microphone, an accelerometer, a gyroscope, an outdoor temperature sensor, a moisture sensor, a laser line tracker sensor, vehicle state sensors, or any other appropriate sensors. In some embodiments, compliance data is received via sensor interface208. In some embodiments, compliance data is received via communications interface206. In various embodiments, vehicle state sensors comprise a speedometer, an accelerator pedal sensor, a brake pedal sensor, an engine revolutions per minute (RPM) sensor, an engine temperature sensor, a headlight sensor, an airbag deployment sensor, driver and passenger seat weight sensors, an anti-locking brake sensor, an engine exhaust sensor, a gear position sensor, a turn signal sensor, a cabin equipment operation sensor, or any other appropriate vehicle state sensors. In some embodiments, sensor interface208comprises an on-board diagnostics (OBD) bus (e.g., society of automotive engineers (SAE) J1939, J1708/J1587, OBD-II, CAN BUS, etc.). In some embodiments, vehicle event recorder200communicates with vehicle state sensors via the OBD bus. In some embodiments, vehicle event recorder200additionally comprises a memory coupled to processor202. In various embodiments, the memory comprises a magnetic memory, an optical memory, a solid-state memory, a flash memory, a non-volatile memory, a read-only memory, a tape memory, a disc memory, or any other appropriate memory.

FIG. 3is a flow diagram illustrating an embodiment of a process for detecting abnormal driver behavior. In some embodiments, the process ofFIG. 3is executed by vehicle event recorder102ofFIG. 1. In the example shown, in300, a sensor data of a vehicle is received. In various embodiments, sensor data comprises location data (e.g., GPS data), accelerometer data, braking data, turning data, fuel data, RPM data, proximity data, or any other appropriate data. In302, a driving behavior is determined based at least in part on the sensor data of the vehicle. In some embodiments, determining a driving behavior comprises determining a set of maneuvers (e.g., a set of maneuvers describing the driving behavior—for example, a braking profile, a turning profile, an acceleration profile, etc.). In304, it is determined whether the driving behavior is abnormal. In some embodiments, determining whether the driving behavior is abnormal comprises comparing the driving behavior to a normal driving behavior. In some embodiments, determining whether the driving behavior is abnormal comprises determining whether a cumulative abnormality is greater than a threshold. In some embodiments, the cumulative abnormality comprises a cumulative abnormality over a period of time (e.g., the cumulative abnormality in the last five minutes, the last hour, etc.). In the event it is determined that the driving behavior is not abnormal, the process ends. In the event it is determined that the driving behavior is abnormal, control passes to306. In306, an abnormal driving behavior is indicated. In some embodiments, instructions for the steps of the process ofFIG. 3are stored in a memory and provided for execution by a processor coupled to the memory. In various embodiments, the memory comprises a magnetic memory, a solid-state memory, an optical memory, or any other appropriate memory.

FIG. 4is a flow diagram illustrating an embodiment of a process for determining a driving behavior based at least in part on sensor data. In some embodiments, the process ofFIG. 4implements302ofFIG. 3. In the example shown, in400, recent vehicle data is determined. In some embodiments, recent vehicle data comprises sensor data received in300ofFIG. 3. In some embodiments, recent vehicle data comprises sensor data received in300ofFIG. 3combined with previous sensor data (e.g., ten seconds of sensor data, one minute of sensor data, ten minutes of sensor data, etc.). In some embodiments, recent vehicle data comprises recent vehicle data for a driver. In402, recent vehicle maneuvers are determined. In some embodiments, recent vehicle maneuvers comprise recent vehicle maneuvers based at least in part on recent vehicle data. In some embodiments, recent vehicle maneuvers comprise recent vehicle maneuvers for a driver.

FIG. 5is a flow diagram illustrating an embodiment of a process for determining whether a driving behavior is abnormal. In some embodiments, the process ofFIG. 5implements304ofFIG. 3. In the example shown, in500, a maneuver abnormality is determined for each maneuver. In some embodiments, the maneuver abnormality comprises the difference between a recent vehicle maneuver and normal vehicle maneuver. In some embodiments, the maneuver abnormality comprises a driver maneuver abnormality (e.g., the maneuver abnormality comprises the difference between a recent vehicle maneuver for a driver and a normal vehicle maneuver for a driver). In502, a cumulative maneuver abnormality is determined. In various embodiments, a cumulative maneuver abnormality comprises an aggregation, a sum, a weighted sum of each maneuver abnormality for a set of maneuvers. In some embodiments, a cumulative maneuver abnormality comprises an aggregation of one or more maneuver abnormalities over a period of time (e.g., the last five minutes, the last hour, the present day, etc.). In504, it is determined whether the cumulative maneuver abnormality is greater than a threshold. In the event it is determined that the cumulative maneuver abnormality is not greater than a threshold, the process ends. In the event it is determined that the cumulative maneuver abnormality is greater than a threshold, control passes to506. In506, the process indicates that the driving behavior is abnormal.

FIG. 6is a flow diagram illustrating an embodiment of a process for determining a normal driving behavior. In some embodiments, the process ofFIG. 4is executed by vehicle event recorder102ofFIG. 1. In some embodiments, a normal driving behavior determined by the process ofFIG. 6is used as part of a process for determining whether a driving behavior is abnormal (e.g., in304ofFIG. 3). In the example shown, in600, a sensor data of a vehicle is received. In some embodiments, the sensor data comprises the sensor data received in300ofFIG. 3. In602, the sensor data of the vehicle is clustered with previous sensor data of the vehicle. In some embodiments, clustering the sensor data comprises adding the sensor data to a sensor data database. In some embodiments, sensor data of the vehicle is clustered with previous sensor data of the vehicle for the driver (e.g., data for different drivers is kept separate). In604normal maneuvers are determined. In some embodiments, normal maneuvers comprise normal maneuvers for a driver. In some embodiments, normal maneuvers comprise normal driving behaviors.

FIG. 7is a flow diagram illustrating an embodiment of a process for determining driving behaviors. In some embodiments, the process ofFIG. 7implements604ofFIG. 6. In some embodiments, the process ofFIG. 7implements402ofFIG. 4. In the example shown, in700, a braking profile is determined. In various embodiments, a braking profile comprises a typical brake application rate, a typical deceleration curve, a typical brake position, or any other appropriate braking profile. In702, a turning profile is determined. In various embodiments, a turning profile comprises a typical steering wheel turning rate, a preferred turning radius, a typical turning path, or any other appropriate turning profile. In704, a following profile is determined. In various embodiments, a following profile comprises an approach rate, a typical following distance, a minimum following distance, a backoff rate, or any other appropriate following profile. In706, a driving speed profile is determined. In various embodiments, a driving speed profile comprises a typical city street driving speed, a typical highway driving speed, a typical driving speed to speed limit ratio, or any other appropriate driving speed profile. In708, an acceleration profile is determined. In various embodiments, an acceleration profile comprises a typical acceleration rate coming up to highway speed, a typical acceleration rate from a stop, a typical acceleration rate when passing another vehicle, or any other appropriate typical acceleration rate. In710, a turn signal profile is determined. In various embodiments, a turn signal profile comprises a typical distance a turn signal is lit before turning on a city street, a typical time a turn signal is lit before changing lanes on a highway, whether a turn signal is used to signal a highway exit, or any other appropriate turn signal profile. In712, a voice profile is determined. In various embodiments, a voice profile comprises a fraction of the time the driver voice is heard, a typical driver voice volume, a typical driver voice tone, whether or not certain words are heard from the driver (e.g., profanity), or any other appropriate voice profile. In714, location specific patterns are determined. In some embodiments, location specific patterns comprise driving maneuvers that are specific to a particular location. In various embodiments, location specific parameters comprise a path for a particular sharp turn, a behavior at a difficult intersection, a driving speed leading into a short merge, or any other appropriate location specific parameters.

In some embodiments, a smile or frown profile is determined.

In some embodiments, abnormal behavior determination comprises determining a difference (e.g., distance) between a normal behavior (e.g., as part of a profile) and a current behavior and summing differences up for different behaviors (e.g., braking behavior, turning behavior, following behavior, driving speed behavior, acceleration behavior, turn signal behavior, voice behavior, location specific pattern behavior, smile behavior, frown behavior, or any other appropriate behavior. In some embodiments, the summation of the difference of behaviors is used in the determination of an abnormal behavior in the event that the summation is greater than a threshold (e.g., a predetermined threshold).

FIG. 8is a flow diagram illustrating an embodiment of a process for indicating a person is not complying. In some embodiments, the process ofFIG. 8implements306ofFIG. 3. In various embodiments, the process executes any or all of the steps ofFIG. 8. In the example shown, in800, a visual indicator is lit (e.g., a light mounted on the vehicle event recorder is turned on). In802, an audio indicator is sounded (e.g., a speaker mounted on the vehicle event recorder beeps, buzzes, etc.). In804, a notification is provided to a mobile device (e.g., a smartphone, a tablet computer, etc.) associated with the person associated with the vehicle. In806, an indication is stored. In808, an indication is uploaded to a vehicle data server (e.g., a vehicle data server as in vehicle data server104ofFIG. 1). In810, a notification is provided to a mobile device associated with a supervisor. In812, a coaching indication to coach the driver is provided. In814, thresholds (e.g., sensor data thresholds for detection of anomalous events) are modified.

In some embodiments, “road rage” behaviors (e.g., aggressive tailgating, quick lane change maneuvers, rapid acceleration/deceleration, etc.) are detected for a driver following a provocation (e.g., being cut off by another vehicle). The behavior is marked as abnormal. An event recorder uploads the event to a server for review. In review, specific behaviors are marked (e.g., aggressive driving, aggressive tailgating, quick lane change maneuvers, rapid acceleration/deceleration, etc.). Upon review, an event is marked for coaching a driver regarding behavior. In various embodiments, coachable event comprise a positive coaching event (e.g., behaviors to encourage) or a negative coaching event (e.g., behaviors to avoid), or any other appropriate behavior for coaching. In various embodiments, thresholds are modified to increase the number of events available for detection of abnormal driving or for coaching or to decrease the number of events available for detection of abnormal driving or for coaching or for any other appropriate reasons for modifying thresholds.