The development of Adaptive Cruise Control (ACC) systems aims to enhance the safety and comfort of vehicles by automatically regulating the speed of the vehicle to ensure a safe gap from the preceding vehicle. However, conventional ACC systems are unable to adapt themselves to changing driving conditions and drivers' behavior. To address this limitation, we propose a Long Short-Term Memory (LSTM) based ACC system that can learn from past driving experiences and adapt and predict new situations in real time. The model is constructed based on the real-world highD dataset, acquired from German highways with the assistance of camera-equipped drones. We evaluated the ACC system under aggressive lane changes when the side lane preceding vehicle cut off, forcing the targeted driver to reduce speed. To this end, the proposed system was assessed on a simulated driving environment and compared with a feedforward Artificial Neural Network (ANN) model and Model Predictive Control (MPC) model. The results show that the LSTM-based system is 19.25% more accurate than the ANN model and 5.9% more accurate than the MPC model in terms of predicting future values of subject vehicle acceleration. The simulation is done in Matlab/Simulink environment.

翻译：自适应巡航控制（ACC）系统的发展旨在通过自动调节车辆速度以确保与前车的安全距离，从而提升车辆的安全性与舒适性。然而，传统ACC系统无法适应变化的驾驶条件及驾驶员行为。为解决此局限性，本文提出一种基于长短期记忆（LSTM）的ACC系统，该系统能够从过往驾驶经验中学习，并在实时场景下适应及预测新情况。模型基于真实世界highD数据集构建，该数据集通过搭载摄像头的无人机在德国高速公路上采集获得。我们评估了侧方车道前车强行切入迫使目标车辆减速的激进变道场景下该ACC系统的性能。为此，在仿真驾驶环境中对所提系统进行了评估，并与前馈人工神经网络（ANN）模型及模型预测控制（MPC）模型进行了对比。结果表明，在预测自车加速度未来值方面，基于LSTM的系统比ANN模型精确度提升19.25%，比MPC模型精确度提升5.9%。仿真实验在Matlab/Simulink环境下完成。