论文标题
使用基于能量的优化的3D对象检测器的合理性验证
Plausibility Verification For 3D Object Detectors Using Energy-Based Optimization
论文作者
论文摘要
通过对象探测器获得的环境感知没有编码在其模型模式中的可预测的安全层，这会产生对系统预测的可信度问题。从最近的对抗攻击可以看出，当前大多数对象检测网络都容易受到输入篡改的影响，在现实世界中，这可能会损害自动驾驶汽车的安全。如果不确定性错误无法传播到子模型，则该问题将会得到更多放大，如果这些错误不属于端到端系统设计的一部分。为了解决这些问题，需要一个平行模块，该模块验证了对对象建议的预测，需要从深处神经网络中提出。这项工作旨在通过提出一个合理的框架来验证Monorun模型的3D对象建议，该框架利用跨传感器流以减少误报。拟议的验证度量使用以四种不同的能量函数的形式使用的先验知识，每个函数都在输出能量值之前利用某个函数，从而为所考虑的假设提供了合理的理由。我们还采用了一种新型的两步模式来改善代表能量模型的复合能函数的优化。
Environmental perception obtained via object detectors have no predictable safety layer encoded into their model schema, which creates the question of trustworthiness about the system's prediction. As can be seen from recent adversarial attacks, most of the current object detection networks are vulnerable to input tampering, which in the real world could compromise the safety of autonomous vehicles. The problem would be amplified even more when uncertainty errors could not propagate into the submodules, if these are not a part of the end-to-end system design. To address these concerns, a parallel module which verifies the predictions of the object proposals coming out of Deep Neural Networks are required. This work aims to verify 3D object proposals from MonoRUn model by proposing a plausibility framework that leverages cross sensor streams to reduce false positives. The verification metric being proposed uses prior knowledge in the form of four different energy functions, each utilizing a certain prior to output an energy value leading to a plausibility justification for the hypothesis under consideration. We also employ a novel two-step schema to improve the optimization of the composite energy function representing the energy model.
