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The development of Adaptive Cruise Control (ACC) systems aims to enhance thesafety and comfort of vehicles by automatically regulating the speed of thevehicle to ensure a safe gap from the preceding vehicle. However, conventionalACC systems are unable to adapt themselves to changing driving conditions anddrivers' behavior. To address this limitation, we propose a Long Short-TermMemory (LSTM) based ACC system that can learn from past driving experiences andadapt and predict new situations in real time. The model is constructed basedon the real-world highD dataset, acquired from German highways with theassistance of camera-equipped drones. We evaluated the ACC system underaggressive lane changes when the side lane preceding vehicle cut off, forcingthe targeted driver to reduce speed. To this end, the proposed system wasassessed on a simulated driving environment and compared with a feedforwardArtificial Neural Network (ANN) model and Model Predictive Control (MPC) model.The results show that the LSTM-based system is 19.25% more accurate than theANN model and 5.9% more accurate than the MPC model in terms of predictingfuture values of subject vehicle acceleration. The simulation is done inMatlab/Simulink environment.

LSTM-based Preceding Vehicle Behaviour Prediction during Aggressive Lane Change for ACC Application