Method and system for real-time estimation of road conditions and vehicle behavior

1. A method for determining a road condition, the method (a) being carried out aboard a vehicle having tires and driving on a road, and (b) comprising:

capturing, using an acoustic sensing system comprising a microphone, a noise signal created by friction between at least one tire and the road;

analyzing, by a processor, the noise signal, to discriminate between (a) a dry road condition, (b) a damp road condition, and (c) a wet road condition, and

determining a friction coefficient between the tires and the road, based on the discrimination between (a) the dry road condition, (b) the damp road condition, and (c) the wet road condition,

wherein the analyzing comprises (1) a first analyzing, by the processor, of the noise signal in a first predetermined frequency range to determine whether the road condition is dry or not, the first analyzing comprising applying a first predetermined threshold to an amplitude of the noise signal in the first predetermined higher frequency range; and (2) when the road condition is not dry, a second analyzing, by the processor, of the noise signal in a second predetermined frequency range having a frequency range lower than that of the first predetermined frequency range to determine whether the road condition is wet or damp, the second analyzing comprising applying a second predetermined threshold to an amplitude of the noise signal in the second predetermined frequency range.

2. The method according to claim 1 , further comprising determining a braking distance of the vehicle on the road, based on the discrimination between (a) the dry road condition, (b) the damp road condition, and (c) the wet road condition.

3. The method according to claim 2 , wherein the determining the braking distance is further based on parameters including vehicle speed and tire wear.

4. The method according to claim 1 , further comprising determining a maximum lateral speed of the vehicle based on the discrimination between (a) the dry road condition, (b) the damp road condition, and (c) the wet road condition.

5. The method according to claim 4 , wherein the determining the maximum lateral speed is further based on parameters including vehicle speed and tire wear.

6. The method according to claim 1 , further comprising:

capturing an image of the road using an optical sensor;

analyzing the image to determine whether some part of the image exhibits different colors; and

based on the analyzing the image, confirming the discrimination between (a) the dry road condition, (b) the damp road condition, and (c) the wet road condition or revising the discrimination between (a) the dry road condition, (b) the damp road condition, and (c) the wet road condition.

7. The method according to claim 1 , further comprising:

obtaining information regarding a weather condition on the road from an external source; and

analyzing the information regarding weather condition to confirm or to revise the discrimination between (a) the dry road condition, (b) the damp road condition, and (c) the wet road condition.

8. The method according to claim 1 , further comprising using at least one camera installed in or on the vehicle to revise the discrimination between (a) the dry road condition, (b) the damp road condition, and (c) the wet road condition.

9. The method according to claim 1 , wherein the determining the friction coefficient is further based on parameters including vehicle speed and tire wear.

10. The method according to claim 1 , further comprising using an infrared spectroscopy sensor to determine water film thickness.