用专家混合架构提升自动驾驶系统感知与决策能力
ExpertAD: Enhancing Autonomous Driving Systems with Mixture of Experts
- 引入感知适配器和稀疏专家混合模块，增强关键特征并减少任务干扰
- 碰撞率降低20%，推理延迟减少25%，在复杂场景中表现更优
- 适合研究自动驾驶多任务协同与高效模型部署的学者和工程师
端到端自动驾驶系统（ADS）在感知与规划方面展现出巨大潜力，但依然面临挑战：复杂驾驶场景中语义信息丰富，但模糊或噪声语义会影响决策可靠性，多任务间的干扰会阻碍最优规划；此外，长推理延迟会延缓决策，增加安全隐患。为此，我们提出ExpertAD框架，采用混合专家（MoE）架构提升ADS性能。通过引入感知适配器（PA）增强任务关键特征，实现情境相关的场景理解；设计稀疏专家混合（MoSE），最小化预测过程中的任务干扰，支持高效规划。实验表明，相比以往方法，ExpertAD平均碰撞率降低20%，推理延迟减少25%。我们在罕见场景（如事故、让行应急车辆）中评估了其多技能规划能力，并验证其对未见城市环境的强大泛化性。此外，通过案例研究展示了其在复杂驾驶场景中的决策过程。
原文摘要 · Abstract (English)
Recent advancements in end-to-end autonomous driving systems (ADSs) underscore their potential for perception and planning capabilities. However, challenges remain. Complex driving scenarios contain rich semantic information, yet ambiguous or noisy semantics can compromise decision reliability, while interference between multiple driving tasks may hinder optimal planning. Furthermore, prolonged inference latency slows decision-making, increasing the risk of unsafe driving behaviors. To address these challenges, we propose ExpertAD, a novel framework that enhances the performance of ADS with Mixture of Experts (MoE) architecture. We introduce a Perception Adapter (PA) to amplify task-critical features, ensuring contextually relevant scene understanding, and a Mixture of Sparse Experts (MoSE) to minimize task interference during prediction, allowing for effective and efficient planning. Our experiments show that ExpertAD reduces average collision rates by up to 20% and inference latency by 25% compared to prior methods. We further evaluate its multi-skill planning capabilities in rare scenarios (e.g., accidents, yielding to emergency vehicles) and demonstrate strong generalization to unseen urban environments. Additionally, we present a case study that illustrates its decision-making process in complex driving scenarios.
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