International Journal of Computer Science and Mechatronics A peer reviewed international journal | Article available at http://ijcsm.in | SJIF 8.05 ©smsamspublications.com | Vol.9.Issue.3.2023.ISSN: 2455-1910 1 | P a g e ©smsamspublications.com A Driving Decision Strategy(DDS) Based on Machine Learning for an Autonomous Vehicle V Hepsiba Sravani 1 , Tummala Srivalli 2 , Naladi Boni Darahas 3 , Kambhampati KumarBrahmaVenkata Sai 4 , Mokka Vinay Kumar 5 , Katragadda Sai Kireeti 6 1 Director Presidency University, Department of Information Science, Department of Computer Science, Department of Computer Data Science, Department of Computer Science and Engineering Presidency University, Bangalore, India. Abstract:A current independent vehicle decides its driving system by thinking about just outer variables (People on foot, street conditions, and so forth.) without considering the inside state of the vehicle. To take care of the issue, this paper proposes "A Driving Decision Strategy(DDS) Based on AI for a self-governing vehicle" which decides the ideal system of a self-governing vehicle by breaking down not just the outer variables, yet additionally the inside elements of the vehicle (consumable conditions, RPM levels and so on. The DDS learns hereditary calculation utilizing sensor information from vehicles put away in the cloud and decides the ideal driving procedure of a self-ruling vehicle. This paper contrasted the DDS and MLP, what's more, RF neural system models to approve the DDS. In the analysis, the DDS had a misfortune rate around 5% lower than existing vehicle entryways and the DDS decided RPM, speed, directing point and path changes 40% quicker than the MLP also, 22% quicker than the RF. Key words:- I. INTRODUCTION However, as the performance of self-driving cars improves, the number of sensors to recognize data is increasing. An increase in these sensors can cause the in- vehicle overload. Self-driving cars use in-vehicle computers to compute data collected by sensors[5]. As the amount of computed data increases, it can affect the speed of judgment and control because of overload. These problems can threaten the stability of the vehicle. To prevent the overload, some studies have developed hardware that can perform deep- running operations inside the vehicle, while others use the cloud to compute the vehicle's sensor data. On the other hand, collected from vehicles to determine how the vehicle is driving. This paper proposes a Driving Decision Strategy(DDS) Based on Machine learning for an autonomous vehicle which reduces the in-vehicle computation by generating’s big data on vehicle driving within the cloud and determines an optimal driving strategy by considering the historical data in the cloud. The proposed DDS analyzes them to determine the best driving strategy by using a Genetic algorithm stored in the Cloud. The DDS learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. This paper compared the DDS with MLP and RF neural network models to validate the DDS. In the experiment, the DDS had a loss rate approximately 5% lower than existing vehicle gateways and the DDS determined RPM, speed, steering angle, and lane changes 40% faster than the MLP and 22% faster than the RF. II. LITERATURE OVERVIEW Y.N. Jeong, S.R.Son, E.H. Jeong, and B.K. Lee, “An Integrated Self- Diagnosis System for an Autonomous Vehicle Based on an IoT Gateway and Deep Learning, ” Applied Sciences, vol. 8, no. 7, July2018. This paper proposes “An Integrated Self-diagnosis System (ISS) for an Autonomous Vehicle based on an Internet of Things (IoT) Gateway and Deep Learning” that collects information from the sensors of an autonomous vehicle, diagnoses itself, and the influence between its parts by using Deep Learning and informs the driver of the result. The ISS consists of three modules. The first In-Vehicle Gateway Module (In-VGM) collects the data from the in- vehicle sensors, consisting of media data like a black box, driving radar, and the control messages of the vehicle, and transfers each of the data collected through each Controller Area Network (CAN), Flex Ray, and Media Oriented Systems Transport (MOST) protocols to the on-board diagnostics (OBD) or the actuators[6]. The data collected from the in-vehicle sensors is transferred to the CAN or Flex Ray protocol and the media data collected while driving is transferred to the MOST protocol. Various types of messages transferred are transformed into a destination protocol message type. The second Optimized Deep Learning Module (ODLM) creates the Training Dataset on the basis of the data collected from the in-vehicle sensors