Constructing vectorized high-definition maps from surround-view cameras has
garnered significant attention in recent years. However, the commonly employed
multi-stage sequential workflow in prevailing approaches often leads to the
loss of early-stage information, particularly in perspective-view features.
Usually, such loss is observed as an instance missing or shape mismatching in
the final birds-eye-view predictions. To address this concern, we propose a
novel approach, namely \textbf{HybriMap}, which effectively exploits clues from
hybrid features to ensure the delivery of valuable information. Specifically,
we design the Dual Enhancement Module, to enable both explicit integration and
implicit modification under the guidance of hybrid features. Additionally, the
perspective keypoints are utilized as supervision, further directing the
feature enhancement process. Extensive experiments conducted on existing
benchmarks have demonstrated the state-of-the-art performance of our proposed
approach.

利用混合特征构建矢量化的高清地图，提出了一种新颖的方法 ——HybriMap，有效地利用混合特征的线索，确保宝贵信息的传递，并利用透视关键点作为监督，进一步指导特征增强过程。通过在现有基准测试上进行广泛实验，证明了我们提出的方法的最新性能。

HybriMap：高效矢量化高清地图构建中的混合线索利用

HybriMap: Hybrid Clues Utilization for Effective Vectorized HD Map  Construction

Surround-view cameras are a primary sensor for automated driving, used for
near-field perception. It is one of the most commonly used sensors in
commercial vehicles primarily used for parking visualization and automated
parking. Four fisheye cameras with a 190{\deg} field of view cover the
360{\deg} around the vehicle. Due to its high radial distortion, the standard
algorithms do not extend easily. Previously, we released the first public
fisheye surround-view dataset named WoodScape. In this work, we release a
synthetic version of the surround-view dataset, covering many of its weaknesses
and extending it. Firstly, it is not possible to obtain ground truth for
pixel-wise optical flow and depth. Secondly, WoodScape did not have all four
cameras annotated simultaneously in order to sample diverse frames. However,
this means that multi-camera algorithms cannot be designed to obtain a unified
output in birds-eye space, which is enabled in the new dataset. We implemented
surround-view fisheye geometric projections in CARLA Simulator matching
WoodScape's configuration and created SynWoodScape. We release 80k images from
the synthetic dataset with annotations for 10+ tasks. We also release the
baseline code and supporting scripts.

本文在 CARLA Simulator 中通过实现 fisheye surround-view 几何投影将 SynWoodScape 数据集扩展，为多项自动驾驶任务提供 80k 图像标注，以解决标准算法无法解决高径向畸变问题的局限性。

SynWoodScape: 自主驾驶用的合成环视鱼眼相机数据集

SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for  Autonomous Driving

Autonomous valet parking is a specific application for autonomous vehicles.
In this task, vehicles need to navigate in narrow, crowded and GPS-denied
parking lots. Accurate localization ability is of great importance. Traditional
visual-based methods suffer from tracking lost due to texture-less regions,
repeated structures, and appearance changes. In this paper, we exploit robust
semantic features to build the map and localize vehicles in parking lots.
Semantic features contain guide signs, parking lines, speed bumps, etc, which
typically appear in parking lots. Compared with traditional features, these
semantic features are long-term stable and robust to the perspective and
illumination change. We adopt four surround-view cameras to increase the
perception range. Assisting by an IMU (Inertial Measurement Unit) and wheel
encoders, the proposed system generates a global visual semantic map. This map
is further used to localize vehicles at the centimeter level. We analyze the
accuracy and recall of our system and compare it against other methods in real
experiments. Furthermore, we demonstrate the practicability of the proposed
system by the autonomous parking application.

研究了利用强大的语义特征在 GPS 无法导航的停车场中构建地图和进行车辆定位的方法，采用四个环视摄像头和惯性测量单元及轮速编码器辅助建立实用的自主泊车系统。

AVP-SLAM: 自主车辆停车场语义化视觉建图与定位

AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous  Vehicles in the Parking Lot

Cameras are an essential part of sensor suite in autonomous driving.
Surround-view cameras are directly exposed to external environment and are
vulnerable to get soiled. Cameras have a much higher degradation in performance
due to soiling compared to other sensors. Thus it is critical to accurately
detect soiling on the cameras, particularly for higher levels of autonomous
driving. We created a new dataset having multiple types of soiling namely
opaque and transparent. It will be released publicly as part of our WoodScape
dataset \cite{yogamani2019woodscape} to encourage further research. We
demonstrate high accuracy using a Convolutional Neural Network (CNN) based
architecture. We also show that it can be combined with the existing object
detection task in a multi-task learning framework. Finally, we make use of
Generative Adversarial Networks (GANs) to generate more images for data
augmentation and show that it works successfully similar to the style transfer.

该研究证明了使用 CNN 和 GANs 的架构来准确检测自动驾驶系统中经常遭受污染的环视摄像头所拍摄的图像，并演示了将该任务与现有物体检测任务相结合的多任务学习框架。