Trajectory planning and tracking control in autonomous driving system: Leveraging machine learning and advanced control algorithms

Abstract

Automated vehicles may soon be seen on our roads as automation is becoming more and more prominent in transportation research. This comprehensive analysis provides a detailed synopsis of the cutting-edge algorithms and technologies propelling the advancement and adoption of autonomous driving. It begins with assessing the fundamental system architectures needed to operate autonomous vehicles: Control over tracking and trajectory planning. Also, this review proceeds to cover in-depth discussions on the decision-making, and trajectory planning techniques that are essential for seamless autonomous vehicle navigation, with a focus on the function of State-of-the-art algorithms, optimization algorithms, machine learning (ML), and deep learning (DL). In addition, Trajectory tracking control methods are also represented in this review, which describes types of tracking control techniques aligned with trajectory planning. Moreover, this review paper also discussed the challenges and limitations of algorithms or techniques implemented in the reviewed paper and suggested some future perspectives. In conclusion, the survey provides an extensive evaluation of the concepts and technologies required to move towards a safe and successful autonomous future, while also documenting the swift advancements in autonomous driving.