Humanoid Trajectory Planning Method for Intelligent Vehicles in Unstructured Environment Based on R-CAM Network

Given that traditional planning methods cannot generate safe and smooth trajectories in complex and unstructured scenarios, one of the crucial challenges is to enable self-driving vehicles to mimic human manipulation behaviors as much as possible. This feature entails the capability to control vehicles safely and smoothly in continuously changing scenarios. Thus, this study proposes a trajectory planning method for humanoid control. First, several professional drivers are invited to maneuver the model vehicle and collect data while driving under obstacle avoidance in outdoor multi-obstacle scenarios. Thereafter, the collected data are processed to establish a dataset representing human manipulation habits. Then, an end-to-end learning approach is used to establish the mapping relationship between the feasible domain space and human manipulation of trajectories. The ResNet compensation attention mechanism (R-CAM) network is used to improve the accuracy of trajectory predictions. Finally, the results of the experimental test analysis with the traditional planning method, two sota methods, and multiple experimental methods show that the trajectory planned by the proposed method closely aligns with human manipulation habits and has superior smoothness. Compared with the baseline method, the root mean square error (RMSE) between the planned trajectory and the real trajectory value in this paper is reduced by 95.5% and 98.8% in the x and y directions, respectively, with higher trajectory precision, and the Var of the angular velocity is decreased by 87.3%, with better smoothness of the planned trajectory.