利用语义感知GNN增强转向角估计
Enhancing Steering Estimation with Semantic-Aware GNNs
AI总结：
本研究针对自动驾驶转向估计任务，提出结合语义感知GNN与RNN的混合架构，利用单目图像生成的伪3D点云及语义优化的图构建策略，在KITTI数据集上较2D模型提升71%性能且成本更低。
摘要：
转向角估计是自动驾驶中的关键任务，传统上依赖基于2D图像的模型。在本研究中，我们探索了通过混合架构引入3D空间信息的优势：该架构结合3D神经网络模型与用于时序建模的循环神经网络（RNNs），以基于LiDAR的点云作为输入。我们系统评估了四种混合3D模型，它们的表现均优于仅使用2D的基线模型，其中图神经网络（Graph Neural Network, GNN）-RNN模型取得了最佳结果。\n为了降低对LiDAR的依赖，我们利用预训练的统一模型从单目图像估计深度，重建伪3D点云。随后我们调整了原本为基于LiDAR的点云设计的GNN-RNN模型，使其适配这些伪3D表征，取得了与基于LiDAR的模型相当甚至更优的性能。此外，该统一模型为每个点提供语义标签，实现了更结构化的场景表征。为进一步优化图构建过程，我们提出了一种高效的连接策略：连接主要在相同语义类别的点之间建立，仅保留20%的类间连接。这种针对性的方法在保留关键空间关系的同时，降低了图复杂度和计算成本。\n最后，我们在KITTI数据集上验证了所提方法，相比仅使用2D的模型实现了71%的性能提升。我们的研究结果凸显了3D空间信息和高效图构建对转向角估计的优势，同时保持了单目图像的成本效益，避免了基于LiDAR的系统的高昂成本。
英文摘要：
Steering estimation is a critical task in autonomous driving, traditionally relying on 2D image-based models. In this work, we explore the advantages of incorporating 3D spatial information through hybrid architectures that combine 3D neural network models with recurrent neural networks (RNNs) for temporal modeling, using LiDAR-based point clouds as input. We systematically evaluate four hybrid 3D models, all of which outperform the 2D-only baseline, with the Graph Neural Network (GNN) - RNN model yielding the best results. To reduce reliance on LiDAR, we leverage a pretrained unified model to estimate depth from monocular images, reconstructing pseudo-3D point clouds. We then adapt the GNN-RNN model, originally designed for LiDAR-based point clouds, to work with these pseudo-3D representations, achieving comparable or even superior performance compared to the LiDAR-based model. Additionally, the unified model provides semantic labels for each point, enabling a more structured scene representation. To further optimize graph construction, we introduce an efficient connectivity strategy where connections are predominantly formed between points of the same semantic class, with only 20\% of inter-class connections retained. This targeted approach reduces graph complexity and computational cost while preserving critical spatial relationships. Finally, we validate our approach on the KITTI dataset, achieving a 71% improvement over 2D-only models. Our findings highlight the advantages of 3D spatial information and efficient graph construction for steering estimation, while maintaining the cost-effectiveness of monocular images and avoiding the expense of LiDAR-based systems.
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