BLOS-BEV: Navigation Map Enhanced Lane Segmentation Network, Beyond Line of Sight

TL;DR

BLOS-BEV introduces a novel BEV segmentation model that integrates SD maps with visual data to enhance beyond line-of-sight perception up to 200 meters, significantly improving long-range scene understanding for autonomous driving.

Contribution

The paper proposes a new BEV segmentation approach combining SD maps with visual data, enabling accurate beyond line-of-sight perception up to 200 meters, surpassing existing methods.

Findings

Achieves state-of-the-art BEV segmentation performance on nuScenes and Argoverse.

Enhances close-range BEV segmentation below 50 meters.

Surpasses other methods by over 20% mIoU at 50-200 meters.

Abstract

Bird's-eye-view (BEV) representation is crucial for the perception function in autonomous driving tasks. It is difficult to balance the accuracy, efficiency and range of BEV representation. The existing works are restricted to a limited perception range within 50 meters. Extending the BEV representation range can greatly benefit downstream tasks such as topology reasoning, scene understanding, and planning by offering more comprehensive information and reaction time. The Standard-Definition (SD) navigation maps can provide a lightweight representation of road structure topology, characterized by ease of acquisition and low maintenance costs. An intuitive idea is to combine the close-range visual information from onboard cameras with the beyond line-of-sight (BLOS) environmental priors from SD maps to realize expanded perceptual capabilities. In this paper, we propose BLOS-BEV, a novel BEV segmentation model that incorporates SD maps for accurate beyond line-of-sight perception, up to 200m. Our approach is applicable to common BEV architectures and can achieve excellent results by incorporating information derived from SD maps. We explore various feature fusion schemes to effectively integrate the visual BEV representations and semantic features from the SD map, aiming to leverage the complementary information from both sources optimally. Extensive experiments demonstrate that our approach achieves state-of-the-art performance in BEV segmentation on nuScenes and Argoverse benchmark. Through multi-modal inputs, BEV segmentation is significantly enhanced at close ranges below 50m, while also demonstrating superior performance in long-range scenarios, surpassing other methods by over 20% mIoU at distances ranging from 50-200m.