  When planning for autonomous driving, it is crucial to consider essential
traffic elements such as lanes, intersections, traffic regulations, and dynamic
agents. However, they are often overlooked by the traditional end-to-end
planning methods, likely leading to inefficiencies and non-compliance with
traffic regulations. In this work, we endeavor to integrate the perception of
these elements into the planning task. To this end, we propose Perception Helps
Planning (PHP), a novel framework that reconciles lane-level planning with
perception. This integration ensures that planning is inherently aligned with
traffic constraints, thus facilitating safe and efficient driving.
Specifically, PHP focuses on both edges of a lane for planning and perception
purposes, taking into consideration the 3D positions of both lane edges and
attributes for lane intersections, lane directions, lane occupancy, and
planning. In the algorithmic design, the process begins with the transformer
encoding multi-camera images to extract the above features and predicting
lane-level perception results. Next, the hierarchical feature early fusion
module refines the features for predicting planning attributes. Finally, the
double-edge interpreter utilizes a late-fusion process specifically designed to
integrate lane-level perception and planning information, culminating in the
generation of vehicle control signals. Experiments on three Carla benchmarks
show significant improvements in driving score of 27.20%, 33.47%, and 15.54%
over existing algorithms, respectively, achieving the state-of-the-art
performance, with the system operating up to 22.57 FPS.
