Holistic Grid Fusion Based Stop Line Estimation

TL;DR

This paper introduces a multi-sensory fusion approach combining stereo cameras and lidar with a specialized neural network to detect stop lines at intersections, significantly extending detection range and robustness under occlusion.

Contribution

It presents a novel multi-sensory fusion method with a tailored neural network architecture for improved stop line detection in complex intersection scenarios.

Findings

Detection range up to 50 meters

Effective under heavy occlusion

Detects stop lines for all lanes

Abstract

Intersection scenarios provide the most complex traffic situations in Autonomous Driving and Driving Assistance Systems. Knowing where to stop in advance in an intersection is an essential parameter in controlling the longitudinal velocity of the vehicle. Most of the existing methods in literature solely use cameras to detect stop lines, which is typically not sufficient in terms of detection range. To address this issue, we propose a method that takes advantage of fused multi-sensory data including stereo camera and lidar as input and utilizes a carefully designed convolutional neural network architecture to detect stop lines. Our experiments show that the proposed approach can improve detection range compared to camera data alone, works under heavy occlusion without observing the ground markings explicitly, is able to predict stop lines for all lanes and allows detection at a distance up to 50 meters.