用跨视角变压器从摄像头图像生成自动驾驶鸟瞰图。
An Initial Study of Bird's-Eye View Generation for Autonomous Vehicles using Cross-View Transformers
- 采用跨视角变换器学习摄像头图像到鸟瞰图的映射。
- 单城训练数据下四摄像头模型在新城镇测试中表现最稳。
- 适合自动驾驶感知系统研发人员参考。
鸟瞰图（BEV）地图为自动驾驶感知提供了结构化的俯视抽象。本文利用跨视角变换器（CVT），基于城市驾驶真实模拟器，将摄像头图像映射到三个BEV通道——道路、车道线和规划轨迹。研究评估了模型在未见过城镇的泛化能力、不同摄像头布局的影响以及两种损失函数（焦点损失与L1损失）的效果。仅使用一个城镇的数据训练时，四摄像头CVT结合L1损失在新城镇测试中表现最为稳健。整体结果凸显了CVT在将摄像头输入映射为合理准确的鸟瞰图方面的潜力。
原文摘要 · Abstract (English)
Bird's-Eye View (BEV) maps provide a structured, top-down abstraction that is crucial for autonomous-driving perception. In this work, we employ Cross-View Transformers (CVT) for learning to map camera images to three BEV's channels - road, lane markings, and planned trajectory - using a realistic simulator for urban driving. Our study examines generalization to unseen towns, the effect of different camera layouts, and two loss formulations (focal and L1). Using training data from only a town, a four-camera CVT trained with the L1 loss delivers the most robust test performance, evaluated in a new town. Overall, our results underscore CVT's promise for mapping camera inputs to reasonably accurate BEV maps.
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