  The implementation of Autonomous Driving (AD) technologies within urban
environments presents significant challenges. These challenges necessitate the
development of advanced perception systems and motion planning algorithms
capable of managing situations of considerable complexity. Although the
end-to-end AD method utilizing LiDAR sensors has achieved significant success
in this scenario, we argue that its drawbacks may hinder its practical
application. Instead, we propose the vision-centric AD as a promising
alternative offering a streamlined model without compromising performance. In
this study, we present a path planning method that utilizes 2D bounding boxes
of objects, developed through imitation learning in urban driving scenarios.
This is achieved by integrating high-definition (HD) map data with images
captured by surrounding cameras. Subsequent perception tasks involve
bounding-box detection and tracking, while the planning phase employs both
local embeddings via Graph Neural Network (GNN) and global embeddings via
Transformer for temporal-spatial feature aggregation, ultimately producing
optimal path planning information. We evaluated our model on the nuPlan
planning task and observed that it performs competitively in comparison to
existing vision-centric methods.
