Application of Uncertainty to Out-of-Distribution Detection for Autonomous Driving Perception Safety

Deep learning is well used in the field of autonomous driving, but systems often encounter scenarios that are significantly different from their training data, known as out-of-distribution (OOD) scenarios. In the realm of autonomous driving safety, perception is the most crucial and complex component, with existing perception network frameworks still suffering from inadequate accuracy, compounded by the plethora of uncertain factors and OOD scenarios in real road environments. However, previous research mainly focused on uncertainty methods and OOD detection techniques, lacking comprehensive reviews that cover their origins in the context of perception safety, theoretical analyses, evaluation metrics, and their applicability in ensuring autonomous driving safety. This review analyzes the OOD and uncertainty issues faced in the autonomous driving perception domain, including some cause analyses and solution s to these issues. Additionally, the review discusses OOD detection evaluation metrics that meet the safety requirements of autonomous driving. Thirdly, it summarizes existing OOD detection methods and explores their strengths and weaknesses under autonomous driving safety with evaluation metrics. Lastly, the article analyzes the application of uncertainty techniques in OOD detection, including the shortcomings and categorization of uncertainty methods, spatial feature utilization, semantic information, and performance optimization techniques. The review also highlights the shortcomings and improvement directions of uncertainty quantification techniques in OOD detection, along with the potential for further integration with autonomous driving scenarios. This study aims to promote the research and application of uncertainty quantification and OOD detection technologies in the context of autonomous driving safety. To facilitate future research, we create a repository that includes links to relevant reviews and methodological code for learning at https://github.com/sotif-ma/OOD-Detection-Methods-and-Datasets