用端到端模型做辅助，实时发现自动驾驶的罕见危险场景。
Integrating End-to-End and Modular Driving Approaches for Online Corner Case Detection in Autonomous Driving
- 在模块化系统中并行运行端到端模型，通过分歧判断异常
- 实车测试显示端到端模型能有效识别罕见危险情况
- 适合关注自动驾驶安全性的研发团队参考
在线罕见场景检测对保障自动驾驶车辆安全性至关重要。当前自动驾驶方法可分为模块化与端到端两类。为融合二者优势，本文提出一种将端到端方法集成到模块化系统的在线罕见场景检测方法。模块化系统负责主驾驶任务，端到端网络作为并行的辅助系统运行，两者输出不一致时即视为潜在罕见场景。我们在真实车辆上实现并开展定性评估。结果表明，具备更强环境感知能力的端到端网络作为辅助系统，能有效参与罕见场景检测，该方法具有提升自动驾驶安全性的潜力。
原文摘要 · Abstract (English)
Online corner case detection is crucial for ensuring safety in autonomous driving vehicles. Current autonomous driving approaches can be categorized into modular approaches and end-to-end approaches. To leverage the advantages of both, we propose a method for online corner case detection that integrates an end-to-end approach into a modular system. The modular system takes over the primary driving task and the end-to-end network runs in parallel as a secondary one, the disagreement between the systems is then used for corner case detection. We implement this method on a real vehicle and evaluate it qualitatively. Our results demonstrate that end-to-end networks, known for their superior situational awareness, as secondary driving systems, can effectively contribute to corner case detection. These findings suggest that such an approach holds potential for enhancing the safety of autonomous vehicles.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。