Design of Neural Network Controller for Automotive Application

Artificial Neural Networks (ANNs) are employed in many areas of industry such as pattern recognition, robotics, controls, medicine, and defense. Their learning and generalization capabilities make them highly desirable solutions for complex problems. However, they are commonly perceived as black boxes since their behavior is typically scattered around its elements with little meaning to an observer. The primary concern in safety critical systems development and assurance is the identification and management of hazards. The application of neural networks in systems where their failure can result in loss of life or property must be backed up with techniques to minimize these undesirable effects. Here, we concentrate on two things through the design of Neural Network controller. Shortest path routing for vehicular network being the first and next is integration of collision avoidance in the same neural network controller. The paper mainly focuses on an algorithm for integrating motion planning and simultaneous localization and mapping (SLAM) designed within neural network controller. Accuracy of the maps and the vehicle locations computed using SLAM is strongly dependent on the characteristics of the environment, for example feature density, as well as the speed and direction of motion of the vehicle. Further the system aims at avoid or mitigate the consequences of an accident