  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).
