Prevalent solutions for Connected and Autonomous vehicle (CAV) mapping
include high definition map (HD map) or real-time Simultaneous Localization and
Mapping (SLAM). Both methods only rely on vehicle itself (onboard sensors or
embedded maps) and can not adapt well to temporarily changed drivable areas
such as work zones. Navigating CAVs in such areas heavily relies on how the
vehicle defines drivable areas based on perception information. Difficulties in
improving perception accuracy and ensuring the correct interpretation of
perception results are challenging to the vehicle in these situations. This
paper presents a prototype that introduces crowdsourcing trajectories
information into the mapping process to enhance CAV's understanding on the
drivable area and traffic rules. A Gaussian Mixture Model (GMM) is applied to
construct the temporarily changed drivable area and occupancy grid map (OGM)
based on crowdsourcing trajectories. The proposed method is compared with SLAM
without any human driving information. Our method has adapted well with the
downstream path planning and vehicle control module, and the CAV did not
violate driving rule, which a pure SLAM method did not achieve.

本文介绍了一种将众包轨迹信息引入到地图制作流程中的原型，以加强 CAV 对可行驶区域和交通规则的理解，利用高斯混合模型构建可行驶区域和占用栅格地图，并比较了该方法与单一 SLAM 方法的优劣。

利用众包车辆轨迹改善工作区域自动驾驶车辆地图和导航

Improving Autonomous Vehicle Mapping and Navigation in Work Zones Using Crowdsourcing Vehicle Trajectories

The real-time segmentation of drivable areas plays a vital role in
accomplishing autonomous perception in cars. Recently there have been some
rapid strides in the development of image segmentation models using deep
learning. However, most of the advancements have been made in model
architecture design. In solving any supervised deep learning problem related to
segmentation, the success of the model that one builds depends upon the amount
and quality of input training data we use for that model. This data should
contain well-annotated varied images for better working of the segmentation
model. Issues like this pertaining to annotations in a dataset can lead the
model to conclude with overwhelming Type I and II errors in testing and
validation, causing malicious issues when trying to tackle real world problems.
To address this problem and to make our model more accurate, dynamic, and
robust, data augmentation comes into usage as it helps in expanding our sample
training data and making it better and more diversified overall. Hence, in our
study, we focus on investigating the benefits of data augmentation by analyzing
pre-existing image datasets and performing augmentations accordingly. Our
results show that the performance and robustness of existing state of the art
(or SOTA) models can be increased dramatically without any increase in model
complexity or inference time. The augmentations decided on and used in this
paper were decided only after thorough research of several other augmentation
methodologies and strategies and their corresponding effects that are in
widespread usage today. All our results are being reported on the widely used
Cityscapes Dataset.

本研究调查了数据增广对城市街景图像数据集中用于实施自动驾驶感知的实时可行驶区域分割模型的性能和鲁棒性的影响。结果表明，采用数据增广可以在不增加模型复杂性或推断时间的情况下显着提高现有技术的性能和鲁棒性。

探索数据增强对可行驶区域分割的影响

Exploring the Effects of Data Augmentation for Drivable Area Segmentation

The joint detection of drivable areas and road anomalies is a crucial task
for ground mobile robots. In recent years, many impressive semantic
segmentation networks, which can be used for pixel-level drivable area and road
anomaly detection, have been developed. However, the detection accuracy still
needs improvement. Therefore, we develop a novel module named the Normal
Inference Module (NIM), which can generate surface normal information from
dense depth images with high accuracy and efficiency. Our NIM can be deployed
in existing convolutional neural networks (CNNs) to refine the segmentation
performance. To evaluate the effectiveness and robustness of our NIM, we embed
it in twelve state-of-the-art CNNs. The experimental results illustrate that
our NIM can greatly improve the performance of the CNNs for drivable area and
road anomaly detection. Furthermore, our proposed NIM-RTFNet ranks 8th on the
KITTI road benchmark and exhibits a real-time inference speed.

本文提出利用一种新颖的模块（NIM）来提高现有卷积神经网络（CNNs）对于自动驾驶中行驶区域和路面异常的检测精度。作者在实验中将我们的 NIM 应用到 12 种现有的 CNNs 上，实验结果表明，我们的 NIM 能够显著提高 CNNs 对于行驶区域和路面异常的检测精度，且在 KITTI 路标奖励上排名第 8，拥有实时推理速度。