Investigation of an Autonomous Vehicle’s using Artificial Neural Network (ANN)
Abel Varghese1, Mahendher Marri2, Sibi Chacko3
1Abel Varghese, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK.
2Mahendher Marri, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK.
3Dr. Sibi Chacko, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK.
Manuscript received on 21 September 2023 | Revised Manuscript received on 29 September 2023 | Manuscript Accepted on 15 October 2023 | Manuscript published on 30 October 2023 | PP: 1-11 | Volume-3 Issue-6, October 2023 | Retrieval Number: 100.1/ijainn.F1072103623 | DOI: 10.54105/ijainn.F1072.103623
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presents a study of autonomous vehicles on a normal highway in UAE, using MATLAB/Simulink® 2022. There is great potential for autonomous vehicles to improve the safety and efficiency of transportation, enhance the quality of time spent in cars, and make transportation more accessible to everyone. The vehicles utilized in the study have three different speeds 40, 80, and 120 km/hr. All vehicles modelled are representative of that available in the UAE. In the model, lane following and lane-keeping assistance functions and Simulink block which are described using artificial neural networks are selected. Simulation is validated with existing published results of physical vehicle models. In the simulations, it is assumed that vehicles have minimal steering angles as the system is in an autonomous collision free environment, selected from MATLAB. Results are obtained as velocities, accelerations, and safe distance with respect to the preceding vehicle. The following results are critically analyzed and validated.
Nomenclature 𝑪𝒇 – Cornering stiffness of front tires, 𝑵/𝒓𝒂𝒅
𝑪𝒓 – Cornering stiffness of front tires, 𝑵/𝒓𝒂𝒅
𝒍𝒇 – Longitudinal distance from CG to front tires, 𝒎
𝒍𝒓 – Longitudinal distance from CG to rear tires, 𝒎
𝒎 – Total mass of the vehicle, 𝒌𝒈
𝑰𝒛 – Yaw moment of inertia of vehicle, 𝒎𝑵𝒔𝟐
Keywords: Artificial Neural Networks, Ackermann, Autonomous Vehicle, Deep Neural Networks, CNN, MATLAB, YOLO.
Scope of the Article: Neural Networks