基准测试与改进自动驾驶中的鸟瞰图感知鲁棒性
Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous Driving
AI总结：
本研究提出RoboBEV基准测试套件，评估33个BEV感知模型在多种相机损坏和传感器故障下的鲁棒性，并发现预训练、无深度BEV变换和时间信息能提升鲁棒性，同时设计了基于CLIP的增强策略。
摘要：
近期，鸟瞰图（BEV）表示法的进展已显示出在车载3D感知方面的巨大潜力。然而，尽管这些方法在标准基准测试中取得了令人瞩目的成果，但它们在各种条件下的鲁棒性仍未得到充分评估。在本研究中，我们提出了RoboBEV，一个旨在评估BEV算法弹性的广泛基准测试套件。该套件包含多种相机损坏类型，每种类型均在三个严重程度级别上进行检验。我们的基准测试还考虑了使用多模态模型时发生的完整传感器故障的影响。通过RoboBEV，我们评估了33个最先进的基于BEV的感知模型，涵盖检测、地图分割、深度估计和占用预测等任务。我们的分析揭示了模型在分布内数据集上的性能与其对分布外挑战的弹性之间存在显著相关性。我们的实验结果还强调了预训练和无深度BEV变换等策略在增强对分布外数据鲁棒性方面的有效性。此外，我们观察到利用广泛的时间信息显著提高了模型的鲁棒性。基于我们的观察，我们设计了一种基于CLIP模型的有效鲁棒性增强策略。本研究的见解为未来无缝结合准确性与现实世界鲁棒性的BEV模型的开发铺平了道路。
英文摘要：
Recent advancements in bird's eye view (BEV) representations have shown remarkable promise for in-vehicle 3D perception. However, while these methods have achieved impressive results on standard benchmarks, their robustness in varied conditions remains insufficiently assessed. In this study, we present RoboBEV, an extensive benchmark suite designed to evaluate the resilience of BEV algorithms. This suite incorporates a diverse set of camera corruption types, each examined over three severity levels. Our benchmarks also consider the impact of complete sensor failures that occur when using multi-modal models. Through RoboBEV, we assess 33 state-of-the-art BEV-based perception models spanning tasks like detection, map segmentation, depth estimation, and occupancy prediction. Our analyses reveal a noticeable correlation between the model's performance on in-distribution datasets and its resilience to out-of-distribution challenges. Our experimental results also underline the efficacy of strategies like pre-training and depth-free BEV transformations in enhancing robustness against out-of-distribution data. Furthermore, we observe that leveraging extensive temporal information significantly improves the model's robustness. Based on our observations, we design an effective robustness enhancement strategy based on the CLIP model. The insights from this study pave the way for the development of future BEV models that seamlessly combine accuracy with real-world robustness.
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