融合端到端与模块化驾驶方法用于自动驾驶中的在线极端情况检测
Integrating End-to-End and Modular Driving Approaches for Online Corner Case Detection in Autonomous Driving
AI总结：
本文提出将端到端网络作为辅助系统与模块化驾驶系统并行运行，利用两者输出不一致实现在线极端情况检测，并在真实车辆上验证了该方法对提升自动驾驶安全性的潜力。
摘要：
在线极端情况检测对于确保自动驾驶车辆的安全至关重要。当前的自动驾驶方法可分为模块化方法和端到端方法。为了利用两者的优势，我们提出了一种将端到端方法集成到模块化系统中的在线极端情况检测方法。模块化系统承担主要驾驶任务，端到端网络作为辅助系统并行运行，两个系统之间的不一致随后被用于极端情况检测。我们在真实车辆上实现了该方法，并对其进行了定性评估。我们的结果表明，端到端网络以其卓越的情境感知能力而闻名，作为辅助驾驶系统，能够有效地促进极端情况检测。这些发现表明，这种方法具有提升自动驾驶车辆安全性的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
