Multi-task learning has emerged as a powerful paradigm to solve a range of
tasks simultaneously with good efficiency in both computation resources and
inference time. However, these algorithms are designed for different tasks
mostly not within the scope of autonomous driving, thus making it hard to
compare multi-task methods in autonomous driving. Aiming to enable the
comprehensive evaluation of present multi-task learning methods in autonomous
driving, we extensively investigate the performance of popular multi-task
methods on the large-scale driving dataset, which covers four common perception
tasks, i.e., object detection, semantic segmentation, drivable area
segmentation, and lane detection. We provide an in-depth analysis of current
multi-task learning methods under different common settings and find out that
the existing methods make progress but there is still a large performance gap
compared with single-task baselines. To alleviate this dilemma in autonomous
driving, we present an effective multi-task framework, VE-Prompt, which
introduces visual exemplars via task-specific prompting to guide the model
toward learning high-quality task-specific representations. Specifically, we
generate visual exemplars based on bounding boxes and color-based markers,
which provide accurate visual appearances of target categories and further
mitigate the performance gap. Furthermore, we bridge transformer-based encoders
and convolutional layers for efficient and accurate unified perception in
autonomous driving. Comprehensive experimental results on the diverse
self-driving dataset BDD100K show that the VE-Prompt improves the multi-task
baseline and further surpasses single-task models.

本文通过对四项常见自动驾驶感知任务（物体检测，语义分割，可行驶区域分割和车道检测）的大规模驾驶数据集的广泛分析，提出了一种有效的多任务学习框架，即 VE-Prompt，该框架通过特定于任务的提示引入视觉示例来指导模型向学习高质量的任务特定表示方向发展，并在视觉上提高了目标类别的精度和性能优越于单任务模型。