Authors: Gaikwad Harsh Ajay, Prof. N. G. Rokade, Prof. M. C. Kolse, Prof. S. K. Said
Abstract: Autonomous vehicles use artificial intelligence to understand their surroundings, decide how to move and control steering, acceleration and braking with minimal or no human intervention. This paper reviews the AI technologies across the full autonomous driving stack. It traces the field from the DARPA challenge vehicles to modern systems and explains the levels of driving automation. Perception with cameras, radar and LiDAR, including deep object detection and point-cloud networks, is examined, followed by localization and mapping through LiDAR odometry and simultaneous localization and mapping, motion planning and control, and learning-based approaches including direct perception, conditional imitation learning, deep reinforcement learning and end-to-end planning-oriented networks. Large-scale datasets and simulation for training and validation are discussed. A five-layer modular architecture for an autonomous vehicle is proposed, together with research gaps, objectives, a comparison of modular and end-to-end approaches and an analysis of challenges including safety validation, rare scenarios, adverse weather, mixed traffic, cybersecurity, ethics and regulation. The review concludes that AI has made autonomous driving technically feasible in defined conditions, but that broad deployment depends on demonstrable safety and robust handling of the unexpected.
DOI: https://doi.org/10.5281/zenodo.24220422
