On Autonomous Vehicle Extrication Strategies under Unstructured Road Surfaces

With the rapid advancement of autonomous driving technology, researchers are increasingly focusing on how these vehicles can self-extricate from rough terrains. This article presents a comprehensive review of domestic and international research on self-extrication strategies for autonomous vehicles in unstructured road environments. It identifies the limitations of current approaches and proposes novel strategies leveraging heuristic learning methods. The study summarizes autonomous extrication strategies for vehicles in diverse scenarios, including road surface identification and subsidence discrimination methods based on wheel-edge acceleration, slip rate, and other dynamic parameters. Additionally, researchers have conducted simulations using software tools like CarSim and Simulink to preliminarily validate the effectiveness of these strategies.