A Human-like Trajectory Learning Approach Fusing Unstructured Scene Feature Extraction with Predictive Goal Point Guidance

The essence of human-like trajectory learning is to construct correspondences between scene elements and temporal trajectory points. Extracting key scene features and setting proper guidance during the learning process are crucial to improving the accuracy of human-like trajectory learning. Therefore, this paper proposes a graph feature extraction method for unstructured scene elements combined with a learning-based two-stage trajectory planner for human-like trajectory generation. The construction of the graph structure considers environmental, trajectory, and waypoint features, with environmental features specifically constructed through pixel clustering and motion compensation to enhance efficiency. In the first stage of the dual-phase trajectory planning, feature extraction is performed using Spatial-Temporal Graph Convo-lutional Networks (ST-GCN), followed by proposal trajectory generation with a sequence to sequence(Seq2Seq) network. In the second stage, the proposed trajectory from the first stage serves as the input, with predicted goal points obtained through the Multilayer Perceptron(MLP) network. The final trajectory is then generated by fusing graph and guidance features. The results demonstrate that the proposed scene graph structure effectively reduces the complexity of the learning network, thereby improving algorithm efficiency. Additionally, heatmap-guided features, jointly generated with the learned predicted goal points and the regularization method, effectively guide trajectory generation and improve the accuracy of human-like trajectory generation.