This study introduces an innovative intelligent tire system capable of estimating
the risk of total hydroplaning based on water pressure measurements within the
tread grooves. Dynamic hydroplaning represents an important safety concern
influenced by water depth, tread design, and vehicle longitudinal speed.
Existing intelligent tire systems primarily assess hydroplaning risk using the
water wedge effect, which occurs predominantly in deep water conditions.
However, in shallow water, which is far more prevalent in real-world scenarios,
the water wedge effect is absent at higher longitudinal speeds, which could make
existing systems unable to reliably assess the total hydroplaning risk. Groove
flow represents a key factor in hydroplaning dynamics, and it is governed by two
mechanisms: water interception rate and water wedge pressure. In both the
shallow water and deep water cases, the groove water flow will increase as a
result of increasing the longitudinal speed of the vehicle for a constant water
depth. Therefore, the water pressure in the tread grooves will also increase as
the longitudinal speed of the vehicle approaches the critical hydroplaning
speed. Unlike conventional systems, the proposed intelligent tire design
utilizes the amplitude and shape of the measured pressure signals from the tread
grooves for estimating the total hydroplaning risk in both shallow and deep
water conditions. Experimental results indicate that peak groove water pressure
increases with the risk of total hydroplaning. Furthermore, the overall shape of
the pressure signal will also be influenced by the total hydroplaning risk. By
addressing the limitations of current intelligent tire systems, the proposed
intelligent tire design offers a robust solution for real-time total
hydroplaning risk estimation across diverse driving conditions.
