  Concurrent processing of multiple autonomous driving 3D perception tasks
within the same spatiotemporal scene poses a significant challenge, in
particular due to the computational inefficiencies and feature competition
between tasks when using traditional multi-task learning approaches. This paper
addresses these issues by proposing a novel unified representation, RepVF,
which harmonizes the representation of various perception tasks such as 3D
object detection and 3D lane detection within a single framework. RepVF
characterizes the structure of different targets in the scene through a vector
field, enabling a single-head, multi-task learning model that significantly
reduces computational redundancy and feature competition. Building upon RepVF,
we introduce RFTR, a network designed to exploit the inherent connections
between different tasks by utilizing a hierarchical structure of queries that
implicitly model the relationships both between and within tasks. This approach
eliminates the need for task-specific heads and parameters, fundamentally
reducing the conflicts inherent in traditional multi-task learning paradigms.
We validate our approach by combining labels from the OpenLane dataset with the
Waymo Open dataset. Our work presents a significant advancement in the
efficiency and effectiveness of multi-task perception in autonomous driving,
offering a new perspective on handling multiple 3D perception tasks
synchronously and in parallel. The code will be available at:
https://github.com/jbji/RepVF
