Objective: Vehicle active safety systems can improve vehicle security by avoiding collisions. An autonomous emergency braking (AEB) system's safety distance calculation is usually based on normal weather conditions. The AEB system's early warning capabilities decrease during adverse weather conditions.
Methods: A multilayer perceptron (MLP) model is used to obtain data from accident and weather data sets. The MLP model is trained and the severity of accidents is predicted. The severity is used as a parameter to build an adaptive AEB system algorithm that considers adverse weather conditions.
Results: The adaptive AEB system algorithm increases safety and reliability under adverse weather conditions. Prescan and a driver-in-the-loop system are used to test the adaptive AEB model. Both tests show that the adaptive AEB model has better performance under adverse weather than the traditional AEB model.
Conclusions: The experimental results demonstrate that the adaptive AEB system can increase the safety distance in rainy weather and avoid collisions under hazy conditions.
Keywords: Autonomous emergency braking; MLP model; accident and weather data set; adverse weather; driver-in-the-loop.