  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.
