论文标题
自动驾驶舰队的分布式多目标跟踪
Distributed Multi-Target Tracking for Autonomous Vehicle Fleets
论文作者
论文摘要
我们基于乘数的交替方向方法提出了可扩展的分布式目标跟踪算法，该方法非常适合通过车辆到车辆网络通信的一组自动驾驶汽车。每种传感车辆都与邻居通信，以执行类似卡尔曼过滤器的更新的迭代，以使每个代理的估计值近似于集中的最大值后验估计，而无需进行测量。我们表明，在固定的通信带宽下，我们的方法优于共识Kalman滤波器恢复集中式估计值。我们还演示了高保真城市驾驶模拟器（CARLA）中的算法，其中使用车载摄像头50个自动驾驶汽车连接在时变通讯网络上的50个目标车辆的位置和速度。
We present a scalable distributed target tracking algorithm based on the alternating direction method of multipliers that is well-suited for a fleet of autonomous cars communicating over a vehicle-to-vehicle network. Each sensing vehicle communicates with its neighbors to execute iterations of a Kalman filter-like update such that each agent's estimate approximates the centralized maximum a posteriori estimate without requiring the communication of measurements. We show that our method outperforms the Consensus Kalman Filter in recovering the centralized estimate given a fixed communication bandwidth. We also demonstrate the algorithm in a high fidelity urban driving simulator (CARLA), in which 50 autonomous cars connected on a time-varying communication network track the positions and velocities of 50 target vehicles using on-board cameras.
