Recent breakthroughs in artificial intelligence offer tremendous promise for
the development of self-driving applications. Deep Neural Networks, in
particular, are being utilized to support the operation of semi-autonomous cars
through object identification and semantic segmentation. To assess the
inadequacy of the current dataset in the context of autonomous and
semi-autonomous cars, we created a new dataset named ANNA. This study discusses
a custom-built dataset that includes some unidentified vehicles in the
perspective of Bangladesh, which are not included in the existing dataset. A
dataset validity check was performed by evaluating models using the
Intersection Over Union (IOU) metric. The results demonstrated that the model
trained on our custom dataset was more precise and efficient than the models
trained on the KITTI or COCO dataset concerning Bangladeshi traffic. The
research presented in this paper also emphasizes the importance of developing
accurate and efficient object detection algorithms for the advancement of
autonomous vehicles.

通过创建新的数据集 ANNA 并借助神经网络，本研究评估了现有数据集在孟加拉交通场景下的不足，并证明了使用该数据集训练的模型在孟加拉交通情境下更精确和高效，强调了开发准确有效的物体检测算法对自动驾驶车辆发展的重要性。

基于深度学习的自动驾驶车辆异构流量数据集 ANNA

ANNA: A Deep Learning Based Dataset in Heterogeneous Traffic for  Autonomous Vehicles

In self-driving applications, LiDAR data provides accurate information about
distances in 3D but lacks the semantic richness of camera data. Therefore,
state-of-the-art methods for perception in urban scenes fuse data from both
sensor types. In this work, we introduce a novel self-supervised method to fuse
LiDAR and camera data for self-driving applications. We build upon masked
autoencoders (MAEs) and train deep learning models to reconstruct masked LiDAR
data from fused LiDAR and camera features. In contrast to related methods that
use birds-eye-view representations, we fuse features from dense spherical LiDAR
projections and features from fish-eye camera crops with a similar field of
view. Therefore, we reduce the learned spatial transformations to moderate
perspective transformations and do not require additional modules to generate
dense LiDAR representations. Code is available at:
this https URL

本研究提出了一种自监督的方法，融合了激光雷达和摄像头数据，训练深度学习模型从融合的数据中恢复遮挡的激光雷达数据，并且通过在稠密球形 LiDAR 投影中融合鱼眼相机裁剪的特征，与使用鸟瞰表示法进行的相关方法相比，减少了视场变换的学习和额外模块的需求。