Garrosa Solana, MaríaOlmeda Santamaría, EsterDíaz López, VicenteMendoza Petit, María Fernanda2022-06-222022-06-222022-02-19Garrosa, M., Olmeda, E., Díaz, V., & Mendoza-Petit, M. F. (2022). Design of an Estimator Using the Artificial Neural Network Technique to Characterise the Braking of a Motor Vehicle. Sensors, 22(4), 1644.1424-3210https://hdl.handle.net/10016/35221Automatic systems are increasingly being applied in the automotive industry to improve driv-ing safety and passenger comfort, reduce traffic, and increase energy efficiency. The objective of this work is focused on improving the automatic brake assistance systems of motor vehicles trying to imitate human behaviour, but correcting possible human errors such as distractions, lack of visibility or time reaction. The proposed system can optimise the intensity of the braking according to the available distance to carry out the manoeuvre and the vehicle speed to be as less aggressive as possible, thus giving priority to the comfort of the driver. A series of tests are car-ried out in this work with a vehicle instrumented with sensors that provide real-time infor-mation about the braking system. The data obtained experimentally during the dynamic tests are used to design an estimator using the Artificial Neural Network (ANN) technique. This in-formation makes it possible to characterise all braking situations based on the pressure of the brake circuit, the type of manoeuvre and the test speed. Thanks to this ANN it is possible to es-timate the requirements of the braking system in real driving situations and carry out the ma-noeuvres automatically. Experiments and simulations verified the proposed method for the es-timation of braking pressure in real deceleration scenarios.30eng© 2021 by the authorsAtribución 3.0 EspañaPressure sensorArtificial neural networkTypes of brakingBrake pressure estimationresearch articleIngeniería Industrial10.3390/s22041644open access1430Sensors22AR/0000030777