Advanced Artificial Neural Network for Steering and Braking Control of Autonomous Electric Vehicle
Haddin M., Budisusila E.N., Prasetyowati S.A.D., Nugroho A.A., Arifin B., Khosyi'in M.
Abstract
Sensors are necessary for an autonomous electric vehicle (AEV) system to identify its environment and take appropriate action, such avoiding obstacles and crashes. Despite their limitations about color, light, and non-metallic items, cameras, radar, and lidar are widely employed to detect objects surrounding a vehicle. Ultrasonic sensors are weather and light-resistant. Thus, the goal of this work was to create object detectors by combining multiple long-range ultrasonic sensors into a multi-sensor circuit. The Arduino processor incorporates an artificial neural network that uses the advanced artificial neural network as a novel approach to control the sensors. There are two steps to this method: offline training and implementation test. The most ideal neural network weights are found offline using the adaptive back propagation algorithm, and the best fixed weight is then embedded into the neural network software on Arduino for implementation test. Because the system can sense more detail about the vehicle's surroundings and accurately avoid obstacles, the definition of actions by taking the object's distance into consideration is better. As a result, the training can produce an output that is closed to the target with 0.001 errors.
No Title
A Comparative Study of Categorical Variable Encoding Techniques for Neural Network Classifiers
Potdar T.S.K., Potdar K.
Machine learning and deep neural network - Artificial intelligence core for lab and real-world test and validation for ADAS and autonomous vehicles: AI for efficient and quality test and validation
Butting B., Muller C., Sax E., Vishnukumar H.J., Butting B., Muller C., Sax E., Vishnukumar H.J., Butting B., Muller C., Sax E., Vishnukumar H.J., Butting B., Muller C., Sax E., Vishnukumar H.J., Butting B., Muller C., Sax E., Vishnukumar H.J.
No Title
Cheng C.-A., Lee K., Pan Y., Saigol K., Cheng C.-A., Lee K., Pan Y., Saigol K.
Steering Control in Electric Power Steering Autonomous Vehicle Using Type-2 Fuzzy Logic Control and PI Control
Arifin B., Arifin B., Nawawi Z., Prasetyowati S.A.D., Suprapto B.Y., Arifin B., Arifin B., Nawawi Z., Prasetyowati S.A.D., Suprapto B.Y., Arifin B., Arifin B., Nawawi Z., Prasetyowati S.A.D., Suprapto B.Y.
Learning in the machine: Random backpropagation and the deep learning channel
Baldi P., Lu Z., Sadowski P., Baldi P., Lu Z., Sadowski P., Baldi P., Lu Z., Sadowski P.
Analysis of artificial intelligence application using back propagation neural network and fuzzy logic controller on wall-following autonomous mobile robot
Adhitya R.Y., Budianto A., Joni K., Khumaidi A., Nurcahya E.D., Pangabidin R., Pratomo I., Rachman I., Soelistijono R.T., Soeprijanto A., Subiyanto L., Syai'In M., Widiawan B., Adhitya R.Y., Budianto A., Joni K., Khumaidi A., Nurcahya E.D., Pangabidin R., Pratomo I., Rachman I., Soelistijono R.T., Soeprijanto A., Subiyanto L., Syai'In M., Widiawan B., Adhitya R.Y., Budianto A., Joni K., Khumaidi A., Nurcahya E.D., Pangabidin R., Pratomo I., Rachman I., Soelistijono R.T., Soeprijanto A., Subiyanto L., Syai'In M., Widiawan B.
Everything you need to know about Neural Networks and Backpropagation
Ognjanovski G., Ognjanovski G., Ognjanovski G.
Neural network training for serial multisensor of autonomous vehicle system
Budisusila E.N., Nawawi Z., Prasetyowati S.A.D., Suprapto B.Y., Budisusila E.N., Nawawi Z., Prasetyowati S.A.D., Suprapto B.Y.
Neural networks in autonomous driving
Mediavilla Z., Montana J.L., Tirnauca C., Mediavilla Z., Montana J.L., Tirnauca C., Mediavilla Z., Montana J.L., Tirnauca C.
