Abstract
The automobile industry has been finding ways to make driving safer and more convenient. We have reached a point where we need to automate the process of driving and make it more affordable. The present implementations are very robotic and cannot run among human-driven vehicles, and this made us realize the need of having a more naturalistic driving style for the autonomous vehicles. This research paper focuses on making a level 3 autonomous vehicle with the help of computer vision and deep learning. The main objective is to achieve the most ideal results in lane detection and object detection which can be worked upon to improve results. The reason that this project is of such great value is the fact that 80%of road accidents happen due to human error and negligence and we believe that providing a driver aid of some sorts could be really helpful in such scenarios. With the help of this paper we will be able to obtain high values with great accuracy with the help of which this can be implemented in real-time vehicles by just making a few alterations and by simple calibrations. We believe this will be a step toward a complete autonomous future.
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Subhashini, J., Khanna, G., Arya, R., Kompalli, L.G.K., Gupta, R. (2022). Naturalistic Driving Simulation Using Automation. In: Reddy, V.S., Prasad, V.K., Wang, J., Reddy, K.T.V. (eds) Soft Computing and Signal Processing. Advances in Intelligent Systems and Computing, vol 1340. Springer, Singapore. https://doi.org/10.1007/978-981-16-1249-7_40
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DOI: https://doi.org/10.1007/978-981-16-1249-7_40
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