International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 05 | May 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 7354 Self Driving Car Using Machine Learning Tej Kurani 1 , Nidhip Kathiriya 2 , Uday Mistry 3 , Prof. Lukesh Kadu 4 , Prof. Harish Motekar 5 1,2,3 Student 4,5 Assistant Professor 1,2,3,4,5 Dept. of Information Technology, Shah and Anchor Kutchhi Engineering College, Mumbai, Maharashtra, India. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - The fundamental idea behind the project is to develop an automated car that can sense its environment and move without human input. This paper proposes Car automation, which is accomplished by recognizing the road, signals, obstacles, stop signs, responding and making decisions, such as changing the course of the vehicle, stopping red signals, stopping signs, and moving on green signals using Neural Network. Self-driving car processes input, tracks a track and sends instructions to the actuators that control acceleration, braking, and steering. The software tracks traffic by means of hard-coded rules, preventive algorithms, predictive modeling, and "smart" discrimination on objects, helping the software to follow rules on transport. Key Words: Self-driving, Neural Network, Actuators, changing course, predictive modeling, preventive algorithms, smart discrimination. 1. INTRODUCTION Human-driven Cars use protection systems to Identify Barriers and stop-offs in some high-end cars, but none of them is completely driverless. The automation feature of existing cars is insufficient to allow cars to drive itself. There is a constant need for drivers, without it the car is inaccessible. But with self-driving cars, we can make the presence of cars on the road constantly. The Driver constantly needs to monitor signals, road safety signs, barriers, and lanes for traditional cars and make decisions accordingly. Self- driving is no longer a futuristic dream, but it is becoming a reality. Companies proclaim their dedication to developing and launching autonomous cars and many of them talk about the level of autonomy being developed. Certainly, autonomous driving can be dangerous to some but it also has its advantages. This would result in reduced traffic congestion, reduced emissions, lower travel costs for all, and a reduction in the cost of new roads and services. It would also immensely improve the mobility of people with old and physical disabilities. (a) (b) Fig -1: Traditional cars(a) and Autonomous cars(b) The fields of expertise are self-driving model cars in a model area with few sensors[6] such as Tesla, and such cars use numerous sensors such as lidar and radar. The way we seek to achieve the autonomy of cars is to model it on RC cars on the 1/10 scale. With the aid of the pi camera and the ultrasonic sensor, the car can sense its environment, and data collected from the two are transmitted on the server through the Raspberry Pi, where we are running the neural network, where the images are being processed to detect lane markings. The car automatically drives on its own according to the lane marking, once it is trained. In actual vehicles, the same algorithm and techniques can be used for automation. 2. RELATED WORK We have studied many papers so far and we have come across the different techniques and technologies which are used to develop the system In literature[1], an autonomous platform for cars, using the softmax function, is presented which squashes the outputs of each unit between 0 to 1. The softmax function acts as a sigmoid function by ranging output, while that is not done by an actual softmax function. The use of a neural network helps to give output in real-time. Before implementing it, they have tested the model on the MATLAB simulator. The system only uses a single camera