论文标题
通过前后驱动样式转移的自主驾驶异常检测的实验
Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers
论文作者
论文摘要
在过去的几年中，自动驾驶社区取得了巨大进展。然而，作为一个关键问题的问题，异常检测是在现实世界中大规模部署自动驾驶汽车的巨大障碍。尽管许多方法，例如不确定性估计或基于分割的图像重新合成，这是极为有希望的，但还有更多的探索。特别受到基于图像重新合成异常检测作品的启发，我们提出了一种通过样式转移进行异常检测的新方法。我们利用生成模型将图像从其原始样式的道路流量域映射到任意型号，然后返回以生成PixelWise Anomaly评分。但是，我们的实验证明了我们的假设错误，我们无法产生重大结果。但是，我们想分享我们的发现，以便其他人可以从我们的实验中学习。
Great progress has been achieved in the community of autonomous driving in the past few years. As a safety-critical problem, however, anomaly detection is a huge hurdle towards a large-scale deployment of autonomous vehicles in the real world. While many approaches, such as uncertainty estimation or segmentation-based image resynthesis, are extremely promising, there is more to be explored. Especially inspired by works on anomaly detection based on image resynthesis, we propose a novel approach for anomaly detection through style transfer. We leverage generative models to map an image from its original style domain of road traffic to an arbitrary one and back to generate pixelwise anomaly scores. However, our experiments have proven our hypothesis wrong, and we were unable to produce significant results. Nevertheless, we want to share our findings, so that others can learn from our experiments.
