预测的输入和输出

预测模块使用来自传感器融合的地图和数据来生成关于所有其他动态对象可能做的预测。为了更清楚地说明，我们来看一个预测输入和输出的例子（json格式）。

示例输入 - 传感器融合

{"timestamp" : 34512.21,"vehicles" : [ { "id" : 0, "x" : -10.0, "y" : 8.1, "v_x" : 8.0, "v_y" : 0.0, "sigma_x" : 0.031, "sigma_y" : 0.040, "sigma_v_x" : 0.12, "sigma_v_y" : 0.03, }, { "id" : 1, "x" : 10.0, "y" : 12.1, "v_x" : -8.0, "v_y" : 0.0, "sigma_x" : 0.031, "sigma_y" : 0.040, "sigma_v_x" : 0.12, "sigma_v_y" : 0.03, }, ] } 

示例输出

{"timestamp" : 34512.21,"vehicles" : [ { "id" : 0, "length": 3.4, "width" : 1.5, "predictions" : [ { "probability" : 0.781, "trajectory" : [ { "x": -10.0, "y": 8.1, "yaw": 0.0, "timestamp": 34512.71 }, { "x": -6.0, "y": 8.1, "yaw": 0.0, "timestamp": 34513.21 }, { "x": -2.0, "y": 8.1, "yaw": 0.0, "timestamp": 34513.71 }, { "x": 2.0, "y": 8.1, "yaw": 0.0, "timestamp": 34514.21 }, { "x": 6.0, "y": 8.1, "yaw": 0.0, "timestamp": 34514.71 }, { "x": 10.0, "y": 8.1, "yaw": 0.0, "timestamp": 34515.21 }, ] }, { "probability" : 0.219, "trajectory" : [ { "x": -10.0, "y": 8.1, "yaw": 0.0, "timestamp": 34512.71 }, { "x": -7.0, "y": 7.5, "yaw": -5.2, "timestamp": 34513.21 }, { "x": -4.0, "y": 6.1, "yaw": -32.0, "timestamp": 34513.71 }, { "x": -3.0, "y": 4.1, "yaw": -73.2, "timestamp": 34514.21 }, { "x": -2.0, "y": 1.2, "yaw": -90.0, "timestamp": 34514.71 }, { "x": -2.0, "y":-2.8, "yaw": -90.0, "timestamp": 34515.21 }, ] } ] }, { "id" : 1, "length": 3.4, "width" : 1.5, "predictions" : [ { "probability" : 1.0, "trajectory" : [ { "x": 10.0, "y": 12.1, "yaw": -180.0, "timestamp": 34512.71 }, { "x": 6.0, "y": 12.1, "yaw": -180.0, "timestamp": 34513.21 }, { "x": 2.0, "y": 12.1, "yaw": -180.0, "timestamp": 34513.71 }, { "x": -2.0, "y": 12.1, "yaw": -180.0, "timestamp": 34514.21 }, { "x": -6.0, "y": 12.1, "yaw": -180.0, "timestamp":