Monitoring and notification of aberrational driver based on time-separated events
Systems and methods for remotely monitoring a driver's behavior, in essentially real time, and providing a notification to the driver when at least two aberrational driver events, which deviate from previously-observed driver actions and/or regulatory limits, are detected. The systems can include a discrepancy calculation module configured to analyze data received from sensors and compare the data to previously-observed driver actions and/or regulatory limits.
1. A method comprising:
receiving sensor data from a plurality of sensors within a vehicle driven by an individual driver;
analyzing the sensor data to detect at least two aberrational driver events from observed driver actions for the individual driver within a time window, the aberrational driver events time-separated and occurring when the observed driver actions for the individual driver satisfy anomaly criteria personalized for the individual driver based on the individual driver's history; and
responsive to the detection of the at least two aberrational driver events, generating a notification to alert the individual driver of the vehicle.
2. The method of claim 1 , further comprising:
dynamically adjusting at least one of the time window and a sampling frequency for the sensor data within the time window based on current conditions.
3. The method of claim 1 , wherein the anomaly criteria are based on a driving behavior pattern of the individual driver.
4. The method of claim 1 , wherein the anomaly criteria are based on a regulatory limit.
5. The method of claim 1 , wherein the observed driver actions include at least one driving activity selected from rapid acceleration, speeding, deceleration or hard braking, erratic braking, and swerving or lateral acceleration.
6. The method of claim 5 , wherein the at least two aberrational driver events are from the same driving activity.
7. The method of claim 5 , wherein the at least two aberrational driver events are from different driving activities.
8. The method of claim 5 , wherein the observed driver actions include at least two driving activities, and the aberrational driver events are based on less than the at least two driving activities.
9. The method of claim 1 , wherein the anomaly criteria is based at least partially on current driving environment.
10. The method of claim 9 , wherein the current driving environment includes any of road conditions, road congestion, weather, day of week, and time of day.
11. The method of claim 1 , wherein the notification is selected based on the detected aberrational driver events.
12. The method of claim 1 , wherein the notification is an audible notification.
13. The method of claim 12 , wherein the audible notification is a verbal message.
14. The method of claim 12 , wherein the audible notification requests a verbal response from the driver.
15. A system for monitoring and notifying a driver, the system comprising:
a discrepancy calculation module stored in memory and executable by a processor, the discrepancy calculation module configured to:
receive sensor data for an individual driver; and
analyze the sensor data to detect at least two aberrational driver events from the data within a time window, the aberrational driver events time-separated and occurring when the data satisfy anomaly criteria personalized for the individual driver and adjusted based on the individual driver's history; and
a notification module stored in memory and executable by the processor, the notification module configured to notify the individual driver upon detection of the at least two aberrational driver events.
16. The system of claim 15 , with the notification module further configured to notify a remote location upon detection of the at least two aberrational driver events.
17. One or more non-transitory computer-readable storage media of a tangible article of manufacture encoding computer-executable instructions for executing on a computer system a computer process, the computer process comprising:
receiving sensor data for an individual driver;
analyzing the sensor data to detect at least two aberrational driver events from the data within a time window, the aberrational driver events time-separated and occurring when the data satisfy anomaly criteria personalized for the individual driver and adjusted based on the individual driver's history; and
notifying the individual driver upon detection of the at least two aberrational driver events.
18. The method of claim 1 , further comprising dynamically modifying the anomaly criteria for a subsequent time window.
19. The method of claim 18 wherein dynamically modifying the anomaly criteria comprises dynamically modifying the anomaly criteria based on the observed driver actions.
20. The system of claim 15 wherein the discrepancy calculation module is further configured to dynamically modify the anomaly criteria for a subsequent time window based on the individual driver's history.