With the increasing complexity of the traffic environment, the importance of
safety perception in intelligent driving is growing. Conventional methods in
the robust perception of intelligent driving focus on training models with
anomalous data, letting the deep neural network decide how to tackle anomalies.
However, these models cannot adapt smoothly to the diverse and complex
real-world environment. This paper proposes a new type of metric known as Eloss
and offers a novel training strategy to empower perception models from the
aspect of anomaly detection. Eloss is designed based on an explanation of the
perception model's information compression layers. Specifically, taking
inspiration from the design of a communication system, the information
transmission process of an information compression network has two
expectations: the amount of information changes steadily, and the information
entropy continues to decrease. Then Eloss can be obtained according to the
above expectations, guiding the update of related network parameters and
producing a sensitive metric to identify anomalies while maintaining the model
performance. Our experiments demonstrate that Eloss can deviate from the
standard value by a factor over 100 with anomalous data and produce distinctive
values for similar but different types of anomalies, showing the effectiveness
of the proposed method. Our code is available at: (code available after paper
accepted).

本文提出了一种新型的指标 Eloss 以及一种新的训练策略，以增强感知模型的异常检测性能。Eloss 是基于感知模型信息压缩层的设计得出的，并以信息传输过程的两个期望为基础：信息量的稳定变化和信息熵的持续降低。实验结果表明，Eloss 在出现异常数据时可以偏离标准值 100 多倍，并针对相似但不同类型的异常生成独特的值。

Eloss in the way: 智能驾驶敏感输入质量指标

Eloss in the way: A Sensitive Input Quality Metrics for Intelligent Driving

Driving datasets accelerate the development of intelligent driving and
related computer vision technologies, while substantial and detailed
annotations serve as fuels and powers to boost the efficacy of such datasets to
improve learning-based models. We propose D$^2$-City, a large-scale
comprehensive collection of dashcam videos collected by vehicles on DiDi's
platform. D$^2$-City contains more than 10000 video clips which deeply reflect
the diversity and complexity of real-world traffic scenarios in China. We also
provide bounding boxes and tracking annotations of 12 classes of objects in all
frames of 1000 videos and detection annotations on keyframes for the remainder
of the videos. Compared with existing datasets, D$^2$-City features data in
varying weather, road, and traffic conditions and a huge amount of elaborate
detection and tracking annotations. By bringing a diverse set of challenging
cases to the community, we expect the D$^2$-City dataset will advance the
perception and related areas of intelligent driving.

我们提出了 D$^2$-City，这是滴滴平台上车辆收集的大规模综合的行车记录仪视频的收集，包含超过 10000 个视频片段，提供了 12 种对象类别的边界框和跟踪注释，并在其余视频的关键帧上提供检测注释，预计该数据集将推动智能驾驶感知和相关领域的发展。