Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers
This addresses safety-critical anomaly detection for autonomous vehicles, but the work is incremental and shares negative findings.
The paper tackled anomaly detection in autonomous driving by proposing a novel approach using forward-backward style transfers with generative models to generate pixelwise anomaly scores, but experiments proved the hypothesis wrong and failed to produce significant results.
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.