Scalable Edge-assisted Fusion and Path Prediction for Connected Autonomous Vehicles
作者: Tyler Landle, Jackson Isenberg, Abhijit Chatterjee, Alexandros Daglis, Umakishore Ramachandran
分类: cs.RO, eess.SY
发布日期: 2026-09-03
💡 一句话要点
提出边缘辅助融合与路径预测以提升联网自动驾驶汽车的规划能力
🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture) 支柱七：动作重定向 (Motion Retargeting) 支柱八：物理动画 (Physics-based Animation)
关键词: 自动驾驶 信息融合 路径预测 边缘计算 智能交通 联网汽车 交通管理
📋 核心要点
- 现有方法主要依赖于每辆AV本地融合信息，导致在参与者数量增加时可扩展性和结果质量受限。
- 本文提出Conductor，一个基于边缘的解决方案，通过固定锚点（如RSU）创建统一世界模型并预测AV轨迹。
- 实验结果显示，Conductor在多种交通场景下满足AoI安全边界，融合精度显著优于随机选择器。
📝 摘要（中文）
自动驾驶汽车（AV）的规划算法依赖于车载传感器的信息，但其视线受限于交通状况和遮挡物。通过边缘辅助创建统一的世界模型，融合来自AV和路边单元（RSU）的信息，并预测AV的未来轨迹，可以提升AV的规划算法，从而改善交通流量和防止碰撞。本文提出的Conductor解决方案通过动态限制参与信息融合的AV数量，确保在严格的信息时效性（AoI）预算内实现高质量的结果。实验表明，该方法在多达31辆CAV的交通场景中，能够满足AoI安全边界，融合精度接近理想状态，且优于随机选择器。
🔬 方法详解
问题定义：本文旨在解决联网自动驾驶汽车（CAV）在动态交通环境中信息融合的时效性和质量问题。现有方法依赖于每辆AV本地融合信息，导致在参与者数量增加时可扩展性不足，结果质量下降。
核心思路：提出的Conductor方案通过边缘计算创建统一的世界模型，动态选择参与信息融合的AV，以确保在严格的AoI时间预算内实现最佳结果。
技术框架：Conductor的整体架构包括信息选择器和运行时控制器。信息选择器根据遮挡情况优先选择能够提供额外信息的AV，而运行时控制器则根据当前交通状况动态调整参与融合的AV数量和轨迹预测数量。
关键创新：最重要的创新在于引入了遮挡感知选择器，该选择器能够优先考虑检测到RSU未覆盖区域物体的AV，从而提升信息融合的质量。
关键设计：在设计中，选择器和控制器的参数设置经过优化，以确保在不同交通场景下都能满足AoI的安全边界。具体的损失函数和网络结构细节在实验中进行了验证和调整。
🖼️ 关键图片
📊 实验亮点
实验结果表明，Conductor在多达31辆CAV的交通场景中，能够满足AoI安全边界，融合精度接近理想状态，且在相同AoI约束下，性能显著优于随机选择器，展示了其在实际应用中的有效性。
🎯 应用场景
该研究的潜在应用领域包括智能交通系统、自动驾驶汽车的路径规划和交通管理。通过提升信息融合的效率和准确性，能够有效改善交通流量，减少碰撞风险，推动智能交通的可持续发展。
📄 摘要（原文）
The planning algorithms inside an Autonomous Vehicle (AV) rely on information from on-board sensors whose line of sight is limited by emerging traffic conditions and occlusions. Edge-assisted creation of a unified world model fusing information from AVs and Road Side Units (RSUs) in a geographical locale, and the prediction of AVs' future trajectories, can enhance the planning algorithms inside AVs to improve quality metrics, such as better traffic flow and collision prevention. AVs participating in such enhancements are called Connected Autonomous Vehicles (CAVs). However, such information generated by the edge (world model and motion predictions) must reach the planners within a tight Age of Information (AoI) time budget to be useful. The state of the art fuses per-CAV information: each AV fuses inputs from other actors locally, which limits both scalability with actor count and quality of results. We present Conductor, an edge-based solution for creating a unified world model from the perspective of a fixed anchor (e.g., an RSU) in a locale and predicting future trajectories of AVs in that locale. Our solution adheres to the AoI time budget by dynamically limiting the number of AVs that would lead to the best quality of results. Specifically, we introduce an occlusion-aware selector that favors information contribution by AVs that detect objects in the locale not covered by RSUs. We pair this selector with a runtime controller that adapts both the number of AV inputs to fuse and the amount of trajectory predictions in each cycle to stay within the AoI time budget. Evaluation on CAV simulation infrastructure shows our joint selector-controller meets the AoI safety bound across traffic scenarios with up to 31 CAVs, with fusion fidelity close to an Oracle and much better than a random selector under the same AoI constraint.