Besides, as actuator faults would deteriorate vehicle safety and stability, they pose a great challenge to the platoon, particularly during cooperative braking. Among many subsystems and functionalities equipped in a vehicle platoon, cooperative braking is a safety‐critical one. The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.Ībstract Vehicle platooning can significantly reduce traffic accidents and enhance transportation safety. Based on these experimental data, the DNN is trained and the performance of the proposed state estimation approach is validated accordingly. The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles. The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing. These techniques are utilized to obtain more accurate model for brake pressure state estimation applications. A deep neural network (DNN) is structured and trained using deep-learning training techniques, such as, dropout and rectified units. In this paper, a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach. However, precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy. As a typical CPS, the braking system is crucial for the vehicle design and safe control. In today's modern electric vehicles, enhancing the safety-critical cyber-physical system (CPS)'s performance is necessary for the safe maneuverability of the vehicle.