Intelligent vehicle systems require a deep understanding of the interplay
between road conditions, surrounding entities, and the ego vehicle's driving
behavior for safe and efficient navigation. This is particularly critical in
developing countries where traffic situations are often dense and unstructured
with heterogeneous road occupants. Existing datasets, predominantly geared
towards structured and sparse traffic scenarios, fall short of capturing the
complexity of driving in such environments. To fill this gap, we present IDD-X,
a large-scale dual-view driving video dataset. With 697K bounding boxes, 9K
important object tracks, and 1-12 objects per video, IDD-X offers comprehensive
ego-relative annotations for multiple important road objects covering 10
categories and 19 explanation label categories. The dataset also incorporates
rearview information to provide a more complete representation of the driving
environment. We also introduce custom-designed deep networks aimed at multiple
important object localization and per-object explanation prediction. Overall,
our dataset and introduced prediction models form the foundation for studying
how road conditions and surrounding entities affect driving behavior in complex
traffic situations.

智能车辆系统需要深入了解道路状况、周围实体和自我驾驶行为之间的相互作用，以实现安全高效的导航，而在发展中国家，交通情况往往密集且无序，道路使用者也异质性强。现有数据集主要针对结构化和稀疏的交通场景，未能捕捉到在这种环境中驾驶的复杂性。为了填补这一空白，我们提出了一个大规模的双视角驾驶视频数据集 IDD-X。通过提供 697K 个边界框、9K 个重要对象轨迹和每个视频 1-12 个对象，IDD-X 为多个重要道路对象提供了综合的自我驾驶相关注释，覆盖了 10 个类别和 19 个解释标签类别。该数据集还结合倒车影像信息，以提供对驾驶环境更全面的表征。我们还引入了专为多个重要对象定位和每个对象解释预测而设计的深度网络。总体而言，我们的数据集和推出的预测模型为研究道路状况和周围实体如何影响复杂交通情况下的驾驶行为奠定了基础。

IDD-X：用于密集和非结构化交通中自我相关重要物体定位和解释的多视图数据集

IDD-X: A Multi-View Dataset for Ego-relative Important Object  Localization and Explanation in Dense and Unstructured Traffic

Changes and advances in information technology have played an important role
in the development of intelligent vehicle systems in recent years. Driver
fatigue and distracted driving are important factors in traffic accidents.
Thus, onboard monitoring of driving behavior has become a crucial component of
advanced driver assistance systems for intelligent vehicles. In this article,
we present techniques for simultaneously detecting fatigue and distracted
driving behaviors using vision-based and machine learning-based approaches. In
driving fatigue detection, we use facial alignment networks to identify facial
feature points in the images, and calculate the distance of the facial feature
points to detect the opening and closing of the eyes and mouth. Furthermore, we
use a convolutional neural network (CNN) based on the MobileNet architecture to
identify various distracted driving behaviors. Experiments are performed on a
PC based setup with a webcam and results are demonstrated using public datasets
as well as custom datasets created for training and testing. Compared to
previous approaches, we build our own datasets and provide better results in
terms of accuracy and computation time.

通过使用基于视觉和基于机器学习的方法，同时检测疲劳和注意力分散的行为，以提高智能车辆系统的驾驶行为监测能力并获得更准确和时间更短的结果。

使用机器学习改进驾驶员疲劳和注意力分散的自动检测

Improving automatic detection of driver fatigue and distraction using  machine learning

High-quality structured data with rich annotations are critical components in
intelligent vehicle systems dealing with road scenes. However, data curation
and annotation require intensive investments and yield low-diversity scenarios.
The recently growing interest in synthetic data raises questions about the
scope of improvement in such systems and the amount of manual work still
required to produce high volumes and variations of simulated data. This work
proposes a synthetic data generation pipeline that utilizes existing datasets,
like nuScenes, to address the difficulties and domain-gaps present in simulated
datasets. We show that using annotations and visual cues from existing
datasets, we can facilitate automated multi-modal data generation, mimicking
real scene properties with high-fidelity, along with mechanisms to diversify
samples in a physically meaningful way. We demonstrate improvements in mIoU
metrics by presenting qualitative and quantitative experiments with real and
synthetic data for semantic segmentation on the Cityscapes and KITTI-STEP
datasets. All relevant code and data is released on github
(this https URL).

本研究提出了一种用已有数据集进行数据注释以生成多模态数据的方法，以优化智能车辆系统中的数据模拟，实现高保真模拟并增加样本多样性并成功改善了语义分割的实验效果。