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Developing AEB control strategy for ADAS vehicles considering vehicles in front and rear

Scientific Reports, Published online: 01 August 2026; doi:10.1038/s41598-026-64688-1 Developing AEB control strategy for ADAS vehicles considering vehicles in front and rear

In modern times, the Automatic Emergency Braking (AEB) system holds significant importance in enhancing vehicle safety due to its efficient detection of forward vehicles and automatic deceleration. While most existing studies on the AEB system primarily concentrate on preventing forward collisions, this research proposes a novel AEB control strategy that integrates both the motion of leading and following vehicles.

The proposed strategy involves constructing a critical deceleration model to prevent rear-end collisions and determining the subject vehicle's deceleration model based on the assessment of forward collision risks. Additionally, the variable trigger threshold of the AEB system is determined based on these models.

When a potential forward collision risk is identified, the system prioritizes braking employing the calculated deceleration for avoiding rear-end collisions. If this deceleration is insufficient to prevent the forward collision, the system automatically switches to the minimum deceleration for mitigating the risk of forward collision, thereby minimizing the threat of a rear-end collision from the following vehicle.

This innovative algorithm was subjected to simulation comparison and verification under typical working conditions. The results demonstrate that, in contrast to the traditional Berkeley model, the proposed strategy effectively incorporates the motion state of the rear vehicle, balances collision risks from both the front and rear, and effectively avoids the possibility of rear-end collisions caused by excessive braking instigated by the AEB system.

This groundbreaking research was made possible through the support of the National Key Research and Development Program of China (2023YFC3009702). The study was conducted by a team comprising Qiao He, Qi Zhao, Yuanyuan Hu, Weijie Xiu, and Kailong Li, and is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Written by urgent.news from Scientific Reports's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

Read the original at nature.com →

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