Strong-Help-Weak: An Online Multi-Task Inference Learning Approach for Robust Advanced Driver Assistance Systems

Multi-task learning in advanced driver assistance systems aims to endow models with the capacity to jointly handle multiple related tasks, such as object detection, depth estimation, and more. However, existing multi-task learning models largely rely on the extensive number of labelled data. In practice, the process of annotating data for multi-task training proves to be exceedingly costly, yet not always accurate. This study introduces an innovative setting named online multi-task inference learning that updates the multi-task model during inference. And we propose a Strong-Help-Weak (SHW) framework which aims to enhance weaker (or more challenging) tasks by leveraging guidance from closely related stronger (or easier) tasks. Specifically, we first build two benchmarks based on KITTI and BDD with four tasks (object detection, object depth estimation, lane line segmentation, and driving area segmentation). Then, we propose two novel modules inspired by two priors: 1) Detection-guided Depth Inference Learning (DetDis) module that leverages the inverse relationship between object size and distance to refine the predicted object distance; and 2) Area-guided Lane Line Inference Learning (AreaLane) module that utilises inclusion relationship between driving area and lane line to infer more accurate lane line. Both modules are efficient and can provide more reliable supervision for the corresponding weaker tasks (object distance estimation and lane line segmentation), respectively. Extensive experiments on the two benchmarks show that our SHW can obtain consistent improvements on the weaker tasks during the inference stage with low computational costs.