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The project aims to implement a real-time lane, object and pedestrian detection system using visual input froma camera mounted on the front of the vehicle. The project is developed using OpenCV based YOLO libraries and Tensorflow. Various objects that appear in the feed of the camera are identified, numbered and labelled so that the self-driving vehicle can maneuver accordingly. To ensure that proposed system is unique in its approach and performs better than existing methodologies, a novel approach was considered wherein the detection of each parameter is segmented, cascaded and simultaneously executed. This novel approach ensures that detection of each parameter remains independent of the other, and the performance and efficiency is at maximum making the system ideal for autonomous vehicles and semi autonomous vehicles. [ABSTRACT FROM AUTHOR]

This paper describes an Artificial Neural Network (ANN) based speed estimation of BLDC drive using the three-phase current of the motor. The star point of three parallel resistors is used as the artificial star point of the motor. The comparators are connected to motor terminals and star point of resistors. This circuit determines the zero crossing of the back-EMF of the motor phases. Using zero crossing detection of the back-EMF with the estimated speed from the ANN, precise control of the BLDC motor is achieved, without the need for a sensor. The ANN is an offline trained network, trained using data obtained from simulations. The network is validated against different Inputs. The network is precise but at the same time simple enough to be implemented on a low-cost processor. The results are validated with simulations done in MATLAB using the trained neural network The findings and applications of neural networks to control the speed of PMSM are very well presented in the referenced papers, but the same for BLDC motors, especially those used in electric vehicles, have not been explored yet. The presented paper attempts to address the challenges and advantages of adopting this sensor-free approach for speed control in EVs. [ABSTRACT FROM AUTHOR]