CoLD Fusion: A Real-time Capable Spline-based Fusion Algorithm for Collective Lane Detection

TL;DR

This paper introduces a real-time spline-based fusion algorithm for collective lane detection that enhances perception range significantly, enabling autonomous vehicles to better perceive their environment even with limited sensor data.

Contribution

The paper presents a novel real-time collective perception method for lane detection using spline-based fusion, extending perception range without relying on HD maps.

Findings

Achieved real-time processing capability.

Extended perception range by up to 200%.

Validated effectiveness across various road types.

Abstract

Comprehensive environment perception is essential for autonomous vehicles to operate safely. It is crucial to detect both dynamic road users and static objects like traffic signs or lanes as these are required for safe motion planning. However, in many circumstances a complete perception of other objects or lanes is not achievable due to limited sensor ranges, occlusions, and curves. In scenarios where an accurate localization is not possible or for roads where no HD maps are available, an autonomous vehicle must rely solely on its perceived road information. Thus, extending local sensing capabilities through collective perception using vehicle-to-vehicle communication is a promising strategy that has not yet been explored for lane detection. Therefore, we propose a real-time capable approach for collective perception of lanes using a spline-based estimation of undetected road sections. We evaluate our proposed fusion algorithm in various situations and road types. We were able to achieve real-time capability and extend the perception range by up to 200%.