System for determining road slipperiness in bad weather conditions
Systems and methods are disclosed for estimating slipperiness of a road surface. This estimate may be obtained using an image sensor mounted on a vehicle. The estimated road slipperiness may be utilized when calculating a risk index for the road, or for an area including the road. If a predetermined threshold for slipperiness is exceeded, corrective actions may be taken. For instance, warnings may be generated to human drivers that are in control of driving vehicle, and autonomous vehicles may automatically adjust vehicle speed based upon road slipperiness detected.
1. A system for determining road slipperiness, the system comprising:
a vehicle-mounted image sensor for a vehicle, the image sensor directed to a road surface of a first road;
a controller that:
receives an image of the road surface from the vehicle-mounted image sensor;
performs a machine learning analysis on the image of the road surface from the vehicle-mounted image sensor to estimate one or more of: (i) a degree to which road markings painted on the road surface have faded, (ii) an age of the first road, or (iii) an age of the road markings painted on the road surface;
receives temperature data from one or more temperature sensors;
estimates a level of slipperiness of the road surface based on both of (i) the machine learning analysis of the image of the road surface, and (ii) the temperature data; and
in response to determining that the estimated level of slipperiness exceeds a predetermined threshold, (i) decelerating the vehicle based on the level of slipperiness of the road surface, and (ii) re-routing the vehicle onto a second road.
2. The system of claim 1 , wherein the controller further: transmits a command to the image sensor to cause the image sensor to capture the image.
3. The system of claim 1 , wherein the controller causes the vehicle to decelerate by actuating a braking system of the vehicle.
4. The system of claim 1 , wherein the controller causes the vehicle to decelerate by reducing a throttle.
5. The system of claim 1 , wherein the image sensor is a laser camera.
6. The system of claim 1 , wherein the image sensor captures video.
7. The system of claim 1 , further comprising a risk index calculating system that calculates a risk index based on the estimated slipperiness of the road surface.
8. A method for determining road slipperiness, the method comprising:
capturing an image of a road surface of a first road via a vehicle-mounted image sensor for a vehicle;
transmitting the captured image to a controller;
analyzing the received image of the road surface via a machine learning analysis of the received image to estimate one or more of: (i) a degree to which road markings painted on the road surface have faded, (ii) an age of the first road, or (iii) an age of the road markings painted on the road surface;
determining temperature data via one or more temperature sensors;
estimating a level of slipperiness of the road surface based on both of (i) the machine learning analysis of the received image of the road surface, and (ii) the temperature data; and
in response to determining that the estimated level of slipperiness exceeds a predetermined threshold, (i) decelerating the vehicle based on the level of slipperiness of the road surface estimated from the machine learning analysis, and (ii) re-routing the vehicle onto a second road.
9. The method of claim 8 , further comprising: before capturing the image of the road surface, transmitting a command from the controller to the image sensor to cause the image sensor to capture the image.
10. The method of claim 8 , wherein causing the vehicle to decelerate comprises: actuating a braking system of the vehicle.
11. The method of claim 8 , causing the vehicle to decelerate comprises: reducing a throttle.
12. The method of claim 8 , wherein the image sensor is a laser camera.
13. The method of claim 8 , wherein the image sensor captures video.
14. The method of claim 8 , further comprising calculating a risk index based on the estimated slipperiness of the road surface.
15. A system for determining road slipperiness, the system comprising:
a means for capturing an image of a road surface of a first road within a first proximity of a vehicle;
a means for performing a machine learning analysis on the image of the road surface to estimate one or more of: (i) a degree to which road markings painted on the road surface have faded, (ii) an age of the first road, or (iii) an age of the road markings painted on the road surface;
a means for determining temperature data within a second proximity of the vehicle;
a means for estimating a level of slipperiness of the road surface based on both of (i) the machine learning analysis of the received image of the road surface, and (ii) the temperature data; and
a means for causing the vehicle to, in response to determining that the estimated level of slipperiness exceeds a predetermined threshold, (i) decelerate based on the level of slipperiness of the road surface, and (ii) re-route onto a second road.
16. The system of claim 15 , wherein the means for causing the vehicle to decelerate comprises: a braking system of the vehicle.
17. The system of claim 15 , wherein the means for causing the vehicle to decelerate comprises: a means for reducing a throttle.
18. The system of claim 15 , wherein the means for capturing the image of the road surface comprises: a laser camera.
19. The system of claim 15 , wherein the means for capturing the image of the road surface comprises: a means for capturing video.
20. The system of claim 15 , further comprising a means for calculating a risk index based on the estimated slipperiness of the road surface.