Capturing uncertainty in object detection is indispensable for safe
autonomous driving. In recent years, deep learning has become the de-facto
approach for object detection, and many probabilistic object detectors have
been proposed. However, there is no summary on uncertainty estimation in deep
object detection, and existing methods are not only built with different
network architectures and uncertainty estimation methods, but also evaluated on
different datasets with a wide range of evaluation metrics. As a result, a
comparison among methods remains challenging, as does the selection of a model
that best suits a particular application. This paper aims to alleviate this
problem by providing a review and comparative study on existing probabilistic
object detection methods for autonomous driving applications. First, we provide
an overview of generic uncertainty estimation in deep learning, and then
systematically survey existing methods and evaluation metrics for probabilistic
object detection. Next, we present a strict comparative study for probabilistic
object detection based on an image detector and three public autonomous driving
datasets. Finally, we present a discussion of the remaining challenges and
future works. Code has been made available at
this https URL

这篇论文对深度学习中目标检测中的不确定性估计进行了综述和比较研究，着重关注了自动驾驶应用中的概率物体检测方法，提供了用于公共数据集的显式比较研究，并讨论了该领域面临的挑战和未来工作。

自动驾驶中概率目标检测的综述和比较研究

A Review and Comparative Study on Probabilistic Object Detection in  Autonomous Driving

Hamilton-Jacobi (HJ) reachability analysis is an important formal
verification method for guaranteeing performance and safety properties of
dynamical control systems. Its advantages include compatibility with general
nonlinear system dynamics, formal treatment of bounded disturbances, and the
ability to deal with state and input constraints. However, it involves solving
a PDE, whose computational and memory complexity scales exponentially with
respect to the number of state variables, limiting its direct use to
small-scale systems. We propose DeepReach, a method that leverages new
developments in sinusoidal networks to develop a neural PDE solver for
high-dimensional reachability problems. The computational requirements of
DeepReach do not scale directly with the state dimension, but rather with the
complexity of the underlying reachable tube. DeepReach achieves comparable
results to the state-of-the-art reachability methods, does not require any
explicit supervision for the PDE solution, can easily handle external
disturbances, adversarial inputs, and system constraints, and also provides a
safety controller for the system. We demonstrate DeepReach on a 9D
multi-vehicle collision problem, and a 10D narrow passage problem, motivated by
autonomous driving applications.

DeepReach 是一种新的方法，利用了正弦网络的新发展，针对高维可达性问题开发了神经偏微分方程求解器，不需要任何显式监督，可以轻松处理外部干扰，对抗性输入和系统约束，并为系统提供安全控制器。在多车碰撞问题和狭窄通道问题上，DeepReach 可以实现与最先进的可达性方法相当的结果，这些问题是受自动驾驶应用启发的。

DeepReach: 高维可达性的深度学习方法

DeepReach: A Deep Learning Approach to High-Dimensional Reachability

We compare the end-to-end training approach to a modular approach in which a
system is decomposed into semantically meaningful components. We focus on the
sample complexity aspect, in the regime where an extremely high accuracy is
necessary, as is the case in autonomous driving applications. We demonstrate
cases in which the number of training examples required by the end-to-end
approach is exponentially larger than the number of examples required by the
semantic abstraction approach.

比较端到端训练和模块化方法的样本复杂性，重点是在需要极高准确度的自动驾驶应用中，我们演示了端到端方法所需的训练样本数量指数级别大于语义抽象方法所需的样本数量。