Path-following Of Autonomous Vehicles Research Articles

Design of a Path-Following Controller for Autonomous Vehicles Using an Optimized Deep Deterministic Policy Gradient Method

The need for a safe and reliable transportation system has made the advancement of autonomous vehicles (Avs) increasingly significant. To achieve Level 5 autonomy, as defined by the Society of Automotive Engineers, AVs must be capable of navigating complex and unconventional traffic environments. Path-following is a crucial task in autonomous driving, requiring precise and safe navigation along a defined path. Traditional path-tracking methods often rely on parameter tuning or rule-based approaches, which may not be suitable for dynamic and complex environments. Reinforcement learning has emerged as a powerful technique for developing effective control strategies through agent-environment interactions. This study investigates the efficiency of an optimized Deep Deterministic Policy Gradient (DDPG) method for controlling acceleration and steering in the path-following of autonomous vehicles. The algorithm demonstrates rapid convergence, enabling stable and efficient path tracking. Additionally, the trained agent achieves smooth control without extreme actions. The performance of the optimized DDPG is compared with the standard DDPG algorithm, with results confirming the improved efficiency of the optimized approach. This advancement could significantly contribute to the development of autonomous driving technology.

Coordinated torque vectoring control and path-following of autonomous vehicles with sideslip angle estimation

This paper proposes a coordinated control strategy that considers torque vectoring control (TVC) and path following system (PFS) for autonomous vehicles with four in-wheel motors. Firstly, a non-linear vehicle state observer based onUniTire tyremodel is proposed to estimate longitudinal and lateral speed. Then the sideslip angle and tyre slip ratio can be obtained. Secondly, a lateral and longitudinal path following algorithm using the driver model based on optimal preview theory and PI controller is established. For the vehicle safety control of autonomous driving, a layered control scheme is proposed, in which the upper layer employs an adaptive second-order sliding mode controller (ASOSM) to get additional yaw moment. Finally, simulation test results prove that the proposed state observation and coordinated control algorithm is valid.

Coordinated torque vectoring control and path-following of autonomous vehicles with sideslip angle estimation

This paper proposes a coordinated control strategy that considers torque vectoring control (TVC) and path following system (PFS) for autonomous vehicles with four in-wheel motors. Firstly, a non-linear vehicle state observer based onUniTire tyremodel is proposed to estimate longitudinal and lateral speed. Then the sideslip angle and tyre slip ratio can be obtained. Secondly, a lateral and longitudinal path following algorithm using the driver model based on optimal preview theory and PI controller is established. For the vehicle safety control of autonomous driving, a layered control scheme is proposed, in which the upper layer employs an adaptive second-order sliding mode controller (ASOSM) to get additional yaw moment. Finally, simulation test results prove that the proposed state observation and coordinated control algorithm is valid.