An Efficient Neural Network Controller for Autonomous Lane-Keeping Assist System

With the advent of new technologies and hardware, the importance and demand for autonomous vehicles are also increasing nowadays. For the safe deployment of these autonomous vehicles, efficient and reliable control techniques are very crucial. Classical control techniques encounter difficulties in accommodating various intricate real-world driving situations, hence, fail to properly control them. Moreover, since a major part of the underlying feedback control computation includes heavy vision computation tasks, classical control techniques are falling short in controlling them properly due to the delay coming from this lengthy vision computation. To overcome this, data-driven approaches are being used in many applications. This research work particularly addresses the challenge of controlling an autonomous lane-keeping assist system leveraging the power of neural networks (NN). The aim of the proposed NN-based controller is to enhance the precision and robustness of the lane-keeping assist system. The NN-based controller demonstrates promising results with respect to some state-of-the-art methodologies, offering hope for safer and more effective autonomous driving in dynamic environments.