Trajectory planning remains one of the key challenges in the large-scale application of autonomous driving technology.For instance,in autonomous driving,lane-changing trajectory planning algorithms are typically built as an optimization process targeting the cost function.However,manually adjusting the feature weights in the cost function to suit diverse traffic scenarios is a highly challenging task.To address this issue,our study proposed a new lane-changing trajectory planning method based on the heterogeneous edge-enhanced spatial-temporal graph attention network(HEST-GAT).Initially,we employed inverse reinforcement learning techniques to extract feature weight vectors of the cost function from a multitude of expert lane-changing demonstrations,thereby constructing an expert-level lane-changing demonstration dataset.Subsequently,traffic scenarios were modeled as heterogeneous directed graphs,where the locations of traffic participants were defined as node attributes,their relative positions as edge attributes,and the types of connections between them as edge types.These attributes and types were combined to form the edge feature representation.To capture the spatial and temporal information within traffic scenes,we utilized the HEST-GAT network for feature extraction,calculating the feature weights of the cost function for each scenario.We then constructed a cost function that integrates trajectory features and feature weights,generating the final lane-changing trajectory plan through an optimization process.To validate the practicality of our proposed method,multiple rounds of lane-changing trajectory planning tests and assessments were conducted on real driving datasets.The results demonstrate that,in comparison to spatial-temporal graph convolutional network methods,lane-changing trajectory planning based on HEST-GAT significantly reduces errors when emulating expert demonstration trajectories.Specifically,errors in longitudinal comfort,longitudinal efficiency,lateral comfort,and safety are reduced by 5.5％,5.4％,1.4％,and 6.0％,respectively.These outcomes prove that our method can generate lane-changing trajectories highly consistent with human driving behavior,exhibiting superior scene adaptability.
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