车载视觉模型通过云端持续更新，提升自动驾驶在新场景下的识别准确率。
AdaptAV: Continuous Adaption of Vision Models for Autonomous Vehicles Using Cloud-based Oracle
- 利用云端高算力运行精准的Oracle模型，指导车端小模型持续重训练。
- 车端模型通过网络接收云端更新，推理准确率随时间逐步提升。
- 适合需要长期优化感知能力的自动驾驶系统研发团队。
在自动驾驶车辆中部署视觉感知模型时，需优先考虑推理速度，导致模型结构较浅、参数较少（即经过更多剪枝）。这类小型模型泛化能力差，在遇到新场景时性能下降明显。本文提出一种系统，通过车辆上传数据，在云端持续重训练视觉模型。利用云端丰富的计算资源（包括机器学习加速器）运行高精度的Oracle模型，指导车载模型的再训练过程。更新后的模型经网络传输至车辆，用于感知任务，从而实现推理准确率随时间持续提升。
原文摘要 · Abstract (English)
Deploying vision perception models in autonomous vehicles requires that we prioritize inference speeds, resulting in a model with shallower architectures and lesser model parameters (i.e., more pruned). Such small models do not generalize well, which could result in poor performance when encountered with novel scenarios. We propose a system that overcomes this by continuously retraining the vision models on the cloud with data uploaded by vehicles. We leverage the abundant compute resources, including machine learning accelerators, of the cloud to run a highly-accurate oracle model that will guide the retraining process of the on-vehicle model. This newly trained model is transmitted to the vehicle over the network and is utilized by the vehicle for perceptions, leading to improved inference accuracy over time.
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