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).

翻译：随着交通环境日益复杂，安全感知在智能驾驶中的重要性不断提升。传统的智能驾驶鲁棒感知方法侧重于使用异常数据训练模型，让深度神经网络自主决策如何处理异常。然而，这些模型无法平滑适应多样且复杂的真实环境。本文提出一种新型度量指标——Eloss，并从异常检测角度提供一种新的训练策略以增强感知模型能力。Eloss基于对感知模型信息压缩层的解释而设计。具体而言，受通信系统设计的启发，信息压缩网络的信息传输过程存在两个期望：信息量平稳变化，且信息熵持续降低。根据上述期望即可推导出Eloss，用于指导相关网络参数的更新，从而在保持模型性能的同时生成用于识别异常的敏感度量。实验表明，Eloss在异常数据上的偏离值可达标准值的百倍以上，并能对相似但不同类型的异常产生区分性数值，验证了所提方法的有效性。相关代码可在（论文接收后提供）获取。