The ADAS & Autonomous Vehicle Technology Summit – North America, which recently took place in San José and brought together over 1,000 senior ADAS/AV professionals, has shown that currently, the market is led by software companies developing autonomous driving platforms that offer road simulation for ADAS/AV systems.

In other words, those systems do not directly measure tire-road grip. Instead, they rely on indirect methods that calculate friction based on vehicle behavior and environmental context.

A primary source of information comes from sensors of ABS (Anti-lock Braking System) and ESC (Electronic Stability Control) systems. These sensors monitor individual wheel speeds and help detect wheel slip, a sign of reduced traction. By analyzing differences in wheel behavior during acceleration, braking, or cornering, systems can estimate when available grip is compromised.

However, in several steady driving situations, such as cruise control mode and autonomous driving on highways, relevant sensor information for modeling is not available.  

Cameras and radar provide visual and environmental context. Cameras can identify surface conditions such as snow, ice, or puddles, while radar can detect changes in road texture. Some software companies at the summit even claimed to use Road Weather Information Systems, satellite images, and weather prognoses to anticipate slippery zones based on historical patterns or real-time conditions.

However, these methods only suggest potential grip issues. They do not measure friction directly.

Despite all these software strategies, current systems fall short of providing real-time, precise friction measurements. The estimation methods rely on complex modeling using a combination of vehicle dynamics data, various sensor fusion, and environmental analysis, which may be seriously inaccurate in rapidly changing conditions such as black ice or sudden rain. Real-time friction varies quickly due to changes in road texture, water film, tire condition, tire wear, and other obstacles, which current ADAS/AD systems can’t accurately track. Instead, they use assumptions, and some driving modes and road conditions may be misleading.

Bridging this gap remains a key challenge for achieving truly reliable information, especially important when it comes to autonomous driving.

To address this gap, new emerging technologies are exploring direct sensing methods. These include tire-embedded sensors that monitor vibration at the contact patch, along with edge AI chips that interpret data from accelerometers of standard TPMS nodes in tires. Such innovations aim to give vehicles the ability to sense road conditions more like a human driver, through feel rather than inference.

While there are a few experimental tire-mounted sensors measuring deformation and contact dynamics, NASP (Neuromorphic Analog Signal Processing) VibroSense TMS chips are the industry’s first practical solution, interpreting vibration data from accelerometers to monitor road surface changes and grip loss in real-time.

As David Shaw, analyst with Tire Industry Research in the United Kingdom, recently noted in his report, the challenge is to measure tire-road grip “on a practical system that costs just a dollar or two, consumes tiny amounts of energy, and needs only limited data bandwidth.”

His conclusion? “None of the top tire makers could achieve that, but POLYN has.”