Environmental condition-based risk level

1. A system for determining risky events, comprising:

an input interface configured to receive sensor data on environmental conditions, wherein the sensor data comprises Global Positioning System (GPS) data;

a processor; and

a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which when executed cause the processor to:

determine whether the environmental conditions indicate an increase in event probability, comprising to:

determine whether an intersection is unknown based on the GPS data;

in response to a determination that the intersection is unknown:

determine an intersection type of the unknown intersection based on three or more of the following:

 determine a road configuration;

 determine a number of roads present;

 determine a number of lanes present;

 determine a number of directions of traffic;

 determine signs;

 determine lights;

 determine pedestrian crosswalks;

 determine bicycle lanes; and/or

 determine rail tracks;

determine a set of intersections of the same intersection type as the unknown intersection;

determine an average risk level of the set of intersections of the same intersection type as the unknown intersection;

determine whether the average risk level is equal to or exceeds a risk threshold level; and

in response to a determination that the average risk level is equal to or exceeds the risk threshold level, determine that the environmental conditions indicate an increase in event probability;

in response to the environmental conditions indicating the increase in the event probability, adjust an event detection threshold, comprising to:

lower the event detection threshold; and

in response to the event detection threshold being met, provide an audible or visual warning as in cab feedback.

2. The system of claim 1 , wherein the sensor data comprises still image data.

3. The system of claim 1 , wherein the sensor data comprises video data.

4. The system of claim 1 , wherein the environmental conditions comprise noises.

5. The system of claim 1 , wherein the environmental conditions comprise intersection complexity.

6. The system of claim 1 , wherein the environmental conditions indicate the increase in the event probability as a result of driver distraction.

7. The system of claim 1 , wherein the environmental conditions indicate the increase in the event probability as a result of driver complexity.

8. The system of claim 1 , wherein the environmental conditions indicate the increase in the event probability as a result of driver stymieing.

9. The system of claim 1 , wherein the environmental conditions indicate the increase in the event probability as a result of a weighted combination of factors.

10. The system of claim 1 , wherein the event detection threshold comprises an event transfer threshold.

11. The system of claim 1 , wherein the event detection threshold comprises an event storage threshold.

12. The system of claim 1 , wherein the event detection threshold comprises a sensor data threshold.

13. The system of claim 12 , wherein the event detection threshold comprises an accelerometer data threshold.

14. The system of claim 12 , wherein the event detection threshold comprises a video data threshold.

15. The system of claim 1 , wherein the determining of the intersection type of the unknown intersection is based on five or more of the following:

determine a road configuration;

determine a number of roads present;

determine a number of lanes present;

determine a number of directions of traffic;

determine signs;

determine lights;

determine pedestrian crosswalks;

determine bicycle lanes; and/or

determine rail tracks.

16. The system of claim 1 , wherein the determining of the intersection type of the unknown intersection is based on seven or more of the following:

determine a road configuration;

determine a number of roads present;

determine a number of lanes present;

determine a number of directions of traffic;

determine signs;

determine lights;

determine pedestrian crosswalks;

determine bicycle lanes; and/or

determine rail tracks.

17. The system of claim 1 , wherein the determining of the intersection type of the unknown intersection is based on the following:

determine a road configuration;

determine a number of roads present;

determine a number of lanes present;

determine a number of directions of traffic;

determine signs;

determine lights;

determine pedestrian crosswalks;

determine bicycle lanes; and

determine rail tracks.

18. A method for determining risky events, comprising

receiving sensor data on environmental conditions, wherein the sensor data comprises Global Positioning System (GPS) data;

determining, using a processor, whether the environmental conditions indicate an increase in event probability, comprising:

determining whether an intersection is unknown based on the GPS data;

in response to a determination that the intersection is unknown:

determining an intersection type of the unknown intersection based on three or more of the following:

determining a road configuration;

determining a number of roads present;

determining a number of lanes present;

determining a number of directions of traffic;

determining signs;

determining lights;

determining pedestrian crosswalks;

determining bicycle lanes; and/or

determining rail tracks;

determining a set of intersections of the same intersection type as the unknown intersection;

determining an average risk level of the set of intersections of the same intersection type as the unknown intersection;

determining whether the average risk level is equal to or exceeds a risk threshold level; and

in response to a determination that the average risk level is equal to or exceeds the risk threshold level, determining that the environmental conditions indicate an increase in event probability;

in response to the environmental conditions indicating the increase in the event probability, adjusting an event detection threshold, comprising:

lowering the event detection threshold; and

in response to the event detection threshold being met, providing an audible or visual warning as in cab feedback.

19. A computer program product for determining risky events, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for:

receiving sensor data on environmental conditions, wherein the sensor data comprises Global Positioning System (GPS) data;

determining whether the environmental conditions indicate an increase in event probability, comprising:

determining whether an intersection is unknown based on the GPS data;

in response to a determination that the intersection is unknown:

determining an intersection type of the unknown intersection based on three or more of the following:

determining a road configuration;

determining a number of roads present;

determining a number of lanes present;

determining a number of directions of traffic;

determining signs;

determining lights;

determining pedestrian crosswalks;

determining bicycle lanes; and/or

determining rail tracks;

determining a set of intersections of the same intersection type as the unknown intersection;

determining an average risk level of the set of intersections of the same intersection type as the unknown intersection;

determining whether the average risk level is equal to or exceeds a risk threshold level; and

in response to a determination that the average risk level is equal to or exceeds the risk threshold level, determining that the environmental conditions indicate an increase in event and probability;

in response to the environmental conditions indicating the increase in the event probability, adjusting an event detection threshold, comprising:

lowering the event detection threshold threshold; and

in response to the event detection threshold being met, providing an audible or visual warning as in cab feedback.