Self-driving vehicles (SDVs) must be rigorously tested on a wide range of
scenarios to ensure safe deployment. The industry typically relies on
closed-loop simulation to evaluate how the SDV interacts on a corpus of
synthetic and real scenarios and verify it performs properly. However, they
primarily only test the system's motion planning module, and only consider
behavior variations. It is key to evaluate the full autonomy system in
closed-loop, and to understand how variations in sensor data based on scene
appearance, such as the shape of actors, affect system performance. In this
paper, we propose a framework, Adv3D, that takes real world scenarios and
performs closed-loop sensor simulation to evaluate autonomy performance, and
finds vehicle shapes that make the scenario more challenging, resulting in
autonomy failures and uncomfortable SDV maneuvers. Unlike prior works that add
contrived adversarial shapes to vehicle roof-tops or roadside to harm
perception only, we optimize a low-dimensional shape representation to modify
the vehicle shape itself in a realistic manner to degrade autonomy performance
(e.g., perception, prediction, and motion planning). Moreover, we find that the
shape variations found with Adv3D optimized in closed-loop are much more
effective than those in open-loop, demonstrating the importance of finding
scene appearance variations that affect autonomy in the interactive setting.

使用实际场景进行闭环传感器模拟以评估自主性能，通过优化车辆形状来研究场景外观变化对自主性能的影响。

Adv3D: 通过封闭环模拟生成安全关键的三维物体

Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop  Simulation

We present a method to infer 3D pose and shape of vehicles from a single
image. To tackle this ill-posed problem, we optimize two-scale projection
consistency between the generated 3D hypotheses and their 2D
pseudo-measurements. Specifically, we use a morphable wireframe model to
generate a fine-scaled representation of vehicle shape and pose. To reduce its
sensitivity to 2D landmarks, we jointly model the 3D bounding box as a coarse
representation which improves robustness. We also integrate three task priors,
including unsupervised monocular depth, a ground plane constraint as well as
vehicle shape priors, with forward projection errors into an overall energy
function.

本文提出了一种从单张图像中推断车辆 3D 姿态和形状的方法，采用可塑性线框模型生成车辆形状和姿态的精细尺度表示，并结合三个任务先验，包括无监督单目深度、地平面约束以及车辆形状先验，通过将前向投影误差整合到总能量函数中优化两级投影一致性来解决这个逆问题。

Mono3D++：具有两种尺度三维假设和任务先验的单目三维车辆检测

Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses  and Task Priors

In this paper, we present an accurate approach to estimate vehicles' pose and
shape from off-board multiview images. The images are taken by monocular
cameras and have small overlaps. We utilize state-of-the-art convolutional
neural networks (CNNs) to extract vehicles' semantic keypoints and introduce a
Cross Projection Optimization (CPO) method to estimate the 3D pose. During the
iterative CPO process, an adaptive shape adjustment method named Hierarchical
Wireframe Constraint (HWC) is implemented to estimate the shape. Our approach
is evaluated under both simulated and real-world scenes for performance
verification. It's shown that our algorithm outperforms other existing
monocular and stereo methods for vehicles' pose and shape estimation. This
approach provides a new and robust solution for off-board visual vehicle
localization and tracking, which can be applied to massive surveillance camera
networks for intelligent transportation.

本文介绍了一种通过卷积神经网络提取车辆关键点，并利用交叉投影优化方法估算 3D 姿态，采用层级线框约束方法估算车辆形状的精确方法，可以应用于智能交通领域的视频监控网络，实现车辆定位和跟踪。实验结果表明，该方法在估算车辆姿态和形状方面表现优于现有的单目和立体方法。