Autonomous driving systems must operate smoothly in human-populated indoor environments, where challenges arise including limited perception and occlusions when relying only on onboard sensors, as well as the need for socially compliant motion planning that accounts for human psychological comfort zones. These factors complicate accurate recognition of human intentions and the generation of comfortable, socially aware trajectories. To address these challenges, we propose SAP-CoPE, an indoor navigation system that integrates cooperative infrastructure with a novel 3D human pose estimation method and a socially-aware model predictive control (MPC)-based motion planner. In the perception module, an optimization problem is formulated to account for uncertainty propagation in the camera projection matrix while enforcing human joint coherence. The proposed method is adaptable to both single- and multi-camera configurations and can incorporate sparse LiDAR point-cloud data. For motion planning, we integrate a psychology inspired personal-space field using the information from estimated human poses into an MPC framework to enhance socially comfort in human-populated environments. Extensive real-world evaluations demonstrate the effectiveness of the proposed approach in generating socially aware trajectories for autonomous systems.

翻译：自动驾驶系统必须在人类活动的室内环境中平稳运行，这些环境存在诸多挑战：仅依赖车载传感器时存在感知受限和遮挡问题，以及需要生成符合社会规范、考虑人类心理舒适区的运动轨迹。这些因素使得准确识别人体意图并生成舒适、社会感知的轨迹变得复杂。为应对这些挑战，我们提出SAP-CoPE——一种集成协同基础设施、新型三维人体姿态估计方法及基于社会感知模型预测控制（MPC）运动规划器的室内导航系统。在感知模块中，我们构建了一个优化问题，该问题在保证人体关节一致性的同时考虑了相机投影矩阵中的不确定性传播。所提方法可适配单相机与多相机配置，并能融合稀疏LiDAR点云数据。在运动规划方面，我们将基于心理学启发的个人空间场与估计的人体姿态信息整合至MPC框架，以提升人机共存环境中的社会舒适度。大量真实场景实验验证了所提方法在生成自主系统社会感知轨迹方面的有效性。