Problem
Research questions and friction points this paper is trying to address.
vision models
autonomous vehicles
inference speeds
generalization
novel scenarios
AdaptAV: Continuous Adaption of Vision Models for Autonomous Vehicles Using Cloud-based Oracle
🤖 AI Summary
为解决自动驾驶视觉模型泛化能力差的问题，提出AdaptAV系统，通过云端高精度模型指导车辆端模型持续再训练，提高推理准确性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Continuous Adaption
Cloud-based Oracle
Vision Models
Autonomous Vehicles
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