General Motors is rethinking how vehicles handle strong crosswinds. In a new patent filed with the US Patent and Trademark Office, GM outlines an advanced crosswind-assist system that doesn’t just react, but predicts and prepares. By using weather data, predictive algorithms, and a sophisticated control unit with both feedback and feedforward mechanisms, the system aims to keep vehicles more stable and composed than traditional reactive setups.
Core Components
The system relies on three primary technologies working together:
- A disturbance quantifier compares predicted and actual vehicle motion to calculate the effect of wind and identify how the vehicle has been disturbed.
- A dynamic observer predicts wind conditions using weather and vehicle data, including:
- Wind speed
- Wind direction
- Gust intensity
- A PIDD module (proportional, integral, double derivative) calculates a control signal to adjust vehicle response based on the severity of the wind.
How It Works
The patent goes further than others in the way it combines the above hardware and logic to enhance car-control systems, particularly with the aim to mitigate the impact of sustained winds and wind gusts on a vehicle. A combination of feedback and feedforward controls based on collected data is used to compensate for wind effects. The system is able to analyze many parameters in order to generate the appropriate control signals, including:
- Wind speed and direction
- Vehicle speed and yaw rate
- Other applicable vehicle data
Why It Matters
This patent addresses problems encountered daily by motorists, especially those living in areas with severe weather and high winds. Based on the information supplied by GM, it can notably improve driver comfort, as well as vehicle handling and stability by mitigating the impact of wind on the vehicle. It’s unique in the way it uses multi-factor compensation to mitigate the effects of both gusty and sustained winds, and stands out for the way in which it uses feedback and feedforward controls, as well as accurate wind prediction.
