Multi-ESO-Based Self-Optimizing Cascade AEB Control Algorithm*

Automatic Emergency Braking(AEB) is a crucial method for enhancing vehicle driving safety. Uncertainty in road conditions, vehicle load, and braking system performance introduces disturbances to the AEB system's behavior. To address this issue, a self-optimizing cascade AEB control algorithm is proposed, which leverages multiple Extended State Observers (ESOs) to effectively mitigate these disturbances. First, upper and lower layer controllers are designed to plan and track the target deceleration respectively. Second, an long short-term memory-based three-dimensional classifier that considers three-axis attitude angle ESO data was designed to identify the vehicle's operating conditions, and the results are used in the cascade controller. Finally, through different condition simulation tests, the stopping distance error of the algorithm is less than 3.5cm. Compared to the standard algorithm, the algorithm effectively enhancing system stability with a precision improvement of 95.1%.