通过车车通信融合车道信息，实时扩展感知范围达200%。
CoLD Fusion: A Real-time Capable Spline-based Fusion Algorithm for Collective Lane Detection
- 基于样条曲线建模未探测路段，实现多车协同车道检测。
- 在多种道路场景下实现毫秒级响应，感知范围最大提升200%。
- 适合无高精地图、定位不精准的自动驾驶场景使用。
自动驾驶的安全运行依赖于对环境的全面感知，需同时识别动态交通参与者和静态物体（如车道线、交通标志）。然而，在传感器视距有限、遮挡或弯道等情况下，完全感知车道往往不可行。当无法实现精确定位或缺乏高清地图时，车辆必须仅依靠自身感知信息进行决策。因此，我们提出一种基于样条曲线的实时车道融合算法（CoLD Fusion），利用车与车之间的通信实现集体感知，补全未探测的车道段。该方法在多种道路类型与场景中进行了评估，验证了其实时性，并成功将感知范围扩展至原有范围的200%。
原文摘要 · Abstract (English)
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%.
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