第三期 预测——2.输入和输出

 

 

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": 34514.71
                        },
                        {
                            "x": -10.0,
                            "y": 12.1,
                            "yaw": -180.0,
                            "timestamp": 34515.21
                        }
                    ]
                }
            ]
        }
    ]
}

笔记

1. 这里显示的预测轨迹仅延伸几秒钟。实际上，我们所做的预测可以延伸到10-20秒的范围。
2. 显示的轨迹具有0.5秒的分辨率。实际上，我们会产生稍微更精细的预测。
3. 这个例子只显示，vehicles但实际上我们也会为所有动态对象产生预测。

 

练习题

左侧车辆有多少种可能的轨迹（id为0）？

• 0

• 1

• 2

• 3+

提交