Collaborative perception has been proven to improve individual perception in autonomous driving through multi-agent interaction. Nevertheless, most methods often assume identical encoders for all agents, which does not hold true when these models are deployed in real-world applications. To realize collaborative perception in actual heterogeneous scenarios, existing methods usually align neighbor features to those of the ego vehicle, which is vulnerable to noise from domain gaps and thus fails to address feature discrepancies effectively. Moreover, they adopt transformer-based modules for domain adaptation, which causes the model inference inefficiency on mobile devices. To tackle these issues, we propose CoDS, a Collaborative perception method that leverages Domain Separation to address feature discrepancies in heterogeneous scenarios. The CoDS employs two feature alignment modules, i.e., Lightweight Spatial-Channel Resizer (LSCR) and Distribution Alignment via Domain Separation (DADS). Besides, it utilizes the Domain Alignment Mutual Information (DAMI) loss to ensure effective feature alignment. Specifically, the LSCR aligns the neighbor feature across spatial and channel dimensions using a lightweight convolutional layer. Subsequently, the DADS mitigates feature distribution discrepancy with encoder-specific and encoder-agnostic domain separation modules. The former removes domain-dependent information and the latter captures task-related information. During training, the DAMI loss maximizes the mutual information between aligned heterogeneous features to enhance the domain separation process. The CoDS employs a fully convolutional architecture, which ensures high inference efficiency. Extensive experiments demonstrate that the CoDS effectively mitigates feature discrepancies in heterogeneous scenarios and achieves a trade-off between detection accuracy and inference efficiency.

翻译：协同感知已被证明能通过多智能体交互提升自动驾驶中的个体感知能力。然而，现有方法通常假设所有智能体采用相同的编码器，这在实际部署中往往不成立。为实现真实异构场景下的协同感知，现有方法通常将邻居特征与自车特征对齐，但这类方法易受域间噪声影响，难以有效处理特征差异。此外，这些方法采用基于Transformer的模块进行域适应，导致模型在移动设备上推理效率低下。为解决这些问题，我们提出CoDS——一种利用域分离处理异构场景特征差异的协同感知方法。CoDS包含两个特征对齐模块：轻量化空间-通道调整器（LSCR）与基于域分离的分布对齐模块（DADS），并采用域对齐互信息（DAMI）损失确保特征对齐效果。具体而言，LSCR通过轻量卷积层在空间和通道维度对齐邻居特征；DADS则通过编码器特定域分离模块和编码器无关域分离模块缓解特征分布差异——前者移除域相关信息，后者提取任务相关信息。训练过程中，DAMI损失通过最大化对齐异构特征间的互信息来增强域分离效果。CoDS采用全卷积架构，确保了高效的推理性能。大量实验表明，CoDS能有效缓解异构场景中的特征差异，并在检测精度与推理效率之间取得平衡。