Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

Abstract

This abstract discusses the significant progress made in autonomous vehicles, focusing on decision‐making systems and control algorithms. It explores recent advances, challenges, and contributions in the field, emphasizing the need for precise navigation and control. The paper covers various methodologies, including rule‐based methods, machine learning, deep learning, probabilistic approaches, and hybrid approaches, examining their applications and effectiveness in ensuring safe navigation. Additionally, it reviews ongoing research efforts, emerging trends, and persistent challenges related to decision‐making and manoeuvre execution in autonomous vehicles, addressing complex topics such as sensor measurement uncertainty, dynamic environment modelling, real‐time responsiveness, and safe interactions with other road users. The objective is to provide a comprehensive overview of the state of the art in autonomous vehicle navigation and control for readers.

Radar Perception of Multi-Object Collision Risk Neural Domains during Autonomous Driving

“…Abdallaoui et al in [29] presented a synthetic analysis of navigation and control methods for autonomous vehicles using maneuvering rules, machine learning, deep learning, probabilistic and hybrid approaches. This takes into account the measurement uncertainty of sensors, modeling of a dynamic environment, real-time response speed and safe interactions with other road users.…”