A fundamental challenge of autonomous driving is maintaining the vehicle in
the center of the lane by adjusting the steering angle. Recent advances
leverage deep neural networks to predict steering decisions directly from
images captured by the car cameras. Machine learning-based steering angle
prediction needs to consider the vehicle's limitation in uploading large
amounts of potentially private data for model training. Federated learning can
address these constraints by enabling multiple vehicles to collaboratively
train a global model without sharing their private data, but it is difficult to
achieve good accuracy as the data distribution is often non-i.i.d. across the
vehicles. This paper presents a new confidence-based federated distillation
method to improve the performance of federated learning for steering angle
prediction. Specifically, it proposes the novel use of entropy to determine the
predictive confidence of each local model, and then selects the most confident
local model as the teacher to guide the learning of the global model. A
comprehensive evaluation of vision-based lane centering shows that the proposed
approach can outperform FedAvg and FedDF by 11.3% and 9%, respectively.

本文提出了一种基于置信度的联邦蒸馏方法，使用熵确定每个本地模型的预测置信度，选择最有信心的本地模型作为教师，以引导全局模型的学习，从而提高自动驾驶中基于视觉的车道中心控制的性能，优于现有的联邦学习算法 FedAvg 和 FedDF。

基于置信度的联邦蒸馏技术应用于基于视觉的车道居中

Confidence-based federated distillation for vision-based lane-centering

Self-driving vehicles have expanded dramatically over the last few years.
Udacity has release a dataset containing, among other data, a set of images
with the steering angle captured during driving. The Udacity challenge aimed to
predict steering angle based on only the provided images. We explore two
different models to perform high quality prediction of steering angles based on
images using different deep learning techniques including Transfer Learning, 3D
CNN, LSTM and ResNet. If the Udacity challenge was still ongoing, both of our
models would have placed in the top ten of all entries.

探讨使用深度学习技术中的 Transfer Learning，3D CNN，LSTM 和 ResNet 来预测自动驾驶车辆的方向盘转角，并在 Udacity 挑战赛中达到前十名。