面向联网自动驾驶汽车的可扩展边缘辅助融合与路径预测

中文摘要 AI 辅助

自动驾驶汽车（AV）内部的规划算法依赖于车载传感器的信息，而传感器的视线会受突发交通状况和遮挡限制。边缘辅助创建统一世界模型，融合某一地理区域内AV与路侧单元（RSU）的信息，并预测AV的未来轨迹，可增强AV内部规划算法，提升交通流量、碰撞预防等质量指标，参与此类增强的AV称为联网自动驾驶汽车（CAV）。但边缘生成的此类信息（世界模型与运动预测）必须在严格的信息年龄（AoI）时间预算内送达规划器才有用。现有技术融合每辆CAV的信息：每辆AV在本地融合其他参与者的输入，这既限制了参与者数量的可扩展性，也影响结果质量。我们提出Conductor，一种基于边缘的解决方案，用于从区域内固定锚点（如RSU）的视角创建统一世界模型，并预测该区域内AV的未来轨迹。我们的方案通过动态限制能带来最佳结果质量的AV数量，以遵守AoI时间预算。具体而言，我们引入感知遮挡的选择器，优先选择检测到RSU未覆盖区域内物体的AV所贡献的信息；将该选择器与运行时控制器配对，该控制器会调整每轮要融合的AV输入数量和轨迹预测数量，以保持在AoI时间预算内。在CAV仿真基础设施上的评估显示，我们的选择器-控制器组合在最多31辆CAV的交通场景中均满足AoI安全边界，融合保真度接近Oracle，且在相同AoI约束下远优于随机选择器。

英文摘要

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.

发表机构

• Georgia Institute of Technology（佐治亚理工学院）
• University of Edinburgh（爱丁堡大学）

机构由 AI 辅助整理，请以论文原文为准。