{
  "sceneNumber": "共测试2个用例",
  "testMileageSum": "1.07公里",
  "testDurationSum": "1分29秒",
  "algorithmComprehensiveScore": 62.46,
  "algorithmLevel": "一般",
  "details": {
    "safe": {
      "name": "安全性",
      "weight": "20.0%",
      "noObjectCar": false,
      "score": 19.75,
      "level": "较差",
      "scoreList": [
        19.75,
        19.75
      ],
      "levelDistribution": {
        "优秀": 0,
        "良好": 0,
        "一般": 0,
        "较差": 100
      },
      "details": {
        "safeDistance": {
          "name": "距离类型",
          "score": 10.0,
          "level": "较差",
          "gradeDistribution": {
            "优秀": 0,
            "良好": 0,
            "一般": 0,
            "较差": 100
          },
          "indexes": {
            "LatSD": {
              "name": "LatSD(yy)",
              "average": 40.0,
              "max": 40.0,
              "min": 40.0,
              "rate": "0.0%",
              "range": "[2.0, inf)"
            }
          },
          "builtin": {
            "LatSD": {
              "name": "LatSD",
              "data": [
                40.0,
                40.0
              ],
              "markLine": [
                2.0
              ]
            }
          },
          "custom": {},
          "description1": "LatSD指标表现良好，平均值在合理范围内且不存在不合格用例；。",
          "description2": "经计算可知，LatSD指标位于合理区间的占比为100.0%。"
        },
        "safeAcceleration": {
          "name": "加速度类型",
          "score": 5.11,
          "level": "较差",
          "gradeDistribution": {
            "优秀": 0,
            "良好": 0,
            "一般": 0,
            "较差": 100
          },
          "indexes": {
            "DRAC": {
              "name": "DRAC(yy)",
              "average": 10.0,
              "max": 10.0,
              "min": 10.0,
              "rate": "100.0%",
              "range": "[0, 5.0]"
            },
            "BTN": {
              "name": "BTN(yy)",
              "average": 40.0,
              "max": 40.0,
              "min": 40.0,
              "rate": "100.0%",
              "range": "[0, 1.0]"
            },
            "STN": {
              "name": "STN(yy)",
              "average": 9.53,
              "max": 9.53,
              "min": 9.53,
              "rate": "100.0%",
              "range": "[0, 1.0]"
            }
          },
          "builtin": {
            "DRAC": {
              "name": "DRAC",
              "data": [
                10.0,
                10.0
              ],
              "markLine": [
                5.0
              ]
            },
            "BTN": {
              "name": "BTN",
              "data": [
                40.0,
                40.0
              ],
              "markLine": [
                1.0
              ]
            },
            "STN": {
              "name": "STN",
              "data": [
                9.53,
                9.53
              ],
              "markLine": [
                1.0
              ]
            }
          },
          "custom": {},
          "description1": "DRAC、BTN和STN指标平均值在合理范围外，算法在大部分用例下均表现不佳，需重点优化。",
          "description2": "经计算可知，DRAC指标位于合理区间的占比为0.0%，BTN指标位于合理区间的占比为0.0%，STN指标位于合理区间的占比为0.0%。"
        },
        "safeProbability": {
          "name": "概率类型",
          "score": 6.1,
          "level": "较差",
          "gradeDistribution": {
            "优秀": 0,
            "良好": 0,
            "一般": 0,
            "较差": 100
          },
          "indexes": {
            "collisionRisk": {
              "name": "碰撞风险程度(yy)",
              "average": 40.0,
              "max": 40.0,
              "min": 40.0,
              "rate": "100.0%",
              "range": "[0, 10.0]"
            },
            "collisionSeverity": {
              "name": "碰撞严重程度(yy)",
              "average": 40.0,
              "max": 40.0,
              "min": 40.0,
              "rate": "100.0%",
              "range": "[0, 10.0]"
            }
          },
          "builtin": {
            "collisionRisk": {
              "name": "collisionRisk",
              "data": [
                40.0,
                40.0
              ],
              "markLine": [
                10.0
              ]
            },
            "collisionSeverity": {
              "name": "collisionSeverity",
              "data": [
                40.0,
                40.0
              ],
              "markLine": [
                10.0
              ]
            }
          },
          "custom": {},
          "description1": "碰撞风险程度和碰撞严重程度指标平均值在合理范围外，算法在大部分用例下均表现不佳，需重点优化。",
          "description2": "经计算可知，碰撞风险程度指标位于合理区间的占比为0.0%，碰撞严重程度指标位于合理区间的占比为0.0%。"
        },
        "customType": {
          "name": "自定义x类型",
          "score": 50.0,
          "level": "较差",
          "gradeDistribution": {
            "优秀": 0,
            "良好": 0,
            "一般": 0,
            "较差": 100
          },
          "indexes": {
            "ldw_miss_warning_count2": {
              "name": "车道偏离漏预警次数2(yy)",
              "average": 0.0,
              "max": 0.0,
              "min": 0.0,
              "rate": "0.0%",
              "range": "[0, 1.0]"
            }
          },
          "builtin": {},
          "custom": {
            "ldw_miss_warning_count2": {
              "name": "ldw_miss_warning_count2",
              "data": [
                0.0,
                0.0
              ],
              "markLine": [
                1.0
              ]
            }
          },
          "description1": "车道偏离漏预警次数2指标表现良好，平均值在合理范围内且不存在不合格用例；。",
          "description2": "经计算可知，车道偏离漏预警次数2指标位于合理区间的占比为100.0%。"
        }
      },
      "description1": "未满足设计指标要求。其中有2个用例得分低于60分，占比为100.00%，需优化算法在距离类型、加速度类型、概率类型和自定义x类型上的表现；",
      "description2": "安全性在距离类型、加速度类型、概率类型和自定义x类型上存在严重风险，需要重点优化。"
    },
    "function": {
      "name": "功能性",
      "weight": "20.0%",
      "noObjectCar": false,
      "score": 46.73,
      "level": "较差",
      "scoreList": [
        46.73,
        46.73
      ],
      "levelDistribution": {
        "优秀": 0,
        "良好": 0,
        "一般": 0,
        "较差": 100
      },
      "details": {
        "functionACC": {
          "name": "ACC",
          "score": 52.73,
          "level": "较差",
          "gradeDistribution": {
            "优秀": 0,
            "良好": 0,
            "一般": 0,
            "较差": 100
          },
          "indexes": {
            "followSpeedDeviation": {
              "name": "跟车速度偏差(yy)",
              "average": 48.52,
              "max": 48.52,
              "min": 48.52,
              "range": "[0, 2.0]"
            },
            "followDistanceDeviation": {
              "name": "跟车距离偏差(yy)",
              "average": 2.86,
              "max": 2.86,
              "min": 2.86,
              "range": "[0, 3.0]"
            },
            "followStopDistance": {
              "name": "跟停距离(yy)",
              "average": 34.63,
              "max": 34.63,
              "min": 34.63,
              "range": "[4.0, inf)"
            }
          },
          "builtin": {
            "followSpeedDeviation": {
              "name": "followSpeedDeviation",
              "data": [
                48.52,
                48.52
              ],
              "markLine": [
                2.0
              ]
            },
            "followDistanceDeviation": {
              "name": "followDistanceDeviation",
              "data": [
                2.86,
                2.86
              ],
              "markLine": [
                3.0
              ]
            },
            "followStopDistance": {
              "name": "followStopDistance",
              "data": [
                34.63,
                34.63
              ],
              "markLine": [
                4.0
              ]
            }
          },
          "custom": {},
          "description1": "跟车距离偏差和跟停距离指标表现良好，平均值在合理范围内且不存在不合格用例；\n跟车速度偏差指标平均值在合理范围外，算法在大部分用例下均表现不佳，需重点优化。",
          "description2": "经计算可知，跟车速度偏差指标位于合理区间的占比为0.0%，跟车距离偏差指标位于合理区间的占比为100.0%，跟停距离指标位于合理区间的占比为100.0%。"
        },
        "functionLKA": {
          "name": "LKA",
          "score": 49.28,
          "level": "较差",
          "gradeDistribution": {
            "优秀": 0,
            "良好": 0,
            "一般": 0,
            "较差": 100
          },
          "indexes": {
            "ldw_miss_warning_count": {
              "name": "车道偏离漏预警次数(yy)",
              "average": 9.61,
              "max": 9.61,
              "min": 9.61,
              "range": "[0, 1.0]"
            },
            "laneDistance": {
              "name": "与近侧车道线的横向距离(yy)",
              "average": 0.01,
              "max": 0.01,
              "min": 0.01,
              "range": "[1.1, inf)"
            },
            "centerDistanceExpectation": {
              "name": "车道中心线横向距离分布期望(yy)",
              "average": 0.12,
              "max": 0.12,
              "min": 0.12,
              "range": "[0, 0.15]"
            },
            "centerDistanceStandardDeviation": {
              "name": "车道中心线横向距离分布标准差(yy)",
              "average": 0.15,
              "max": 0.15,
              "min": 0.15,
              "range": "[0, 0.2]"
            },
            "centerDistanceMax": {
              "name": "车道中心线横向距离分布最大值(yy)",
              "average": 0.74,
              "max": 0.74,
              "min": 0.74,
              "range": "[0, 0.5]"
            },
            "centerDistanceMin": {
              "name": "车道中心线横向距离分布最小值(yy)",
              "average": 0.0,
              "max": 0.0,
              "min": 0.0,
              "range": "[0, 0.5]"
            },
            "centerDistanceFrequency": {
              "name": "横向相对位置震荡频率(yy)",
              "average": 0.57,
              "max": 0.57,
              "min": 0.57,
              "range": "[0, 0.1]"
            },
            "centerDistanceRange": {
              "name": "横向相对位置震荡极差(yy)",
              "average": 0.64,
              "max": 0.64,
              "min": 0.64,
              "range": "[0, 0.7]"
            }
          },
          "builtin": {
            "laneDistance": {
              "name": "laneDistance",
              "data": [
                0.01,
                0.01
              ],
              "markLine": [
                1.1
              ]
            },
            "centerDistanceExpectation": {
              "name": "centerDistanceExpectation",
              "data": [
                0.12,
                0.12
              ],
              "markLine": [
                0.15
              ]
            },
            "centerDistanceStandardDeviation": {
              "name": "centerDistanceStandardDeviation",
              "data": [
                0.15,
                0.15
              ],
              "markLine": [
                0.2
              ]
            },
            "centerDistanceMax": {
              "name": "centerDistanceMax",
              "data": [
                0.74,
                0.74
              ],
              "markLine": [
                0.5
              ]
            },
            "centerDistanceMin": {
              "name": "centerDistanceMin",
              "data": [
                0.0,
                0.0
              ],
              "markLine": [
                0.5
              ]
            },
            "centerDistanceFrequency": {
              "name": "centerDistanceFrequency",
              "data": [
                0.57,
                0.57
              ],
              "markLine": [
                0.1
              ]
            },
            "centerDistanceRange": {
              "name": "centerDistanceRange",
              "data": [
                0.64,
                0.64
              ],
              "markLine": [
                0.7
              ]
            }
          },
          "custom": {
            "ldw_miss_warning_count": {
              "name": "ldw_miss_warning_count",
              "data": [
                9.61,
                9.61
              ],
              "markLine": [
                1.0
              ]
            }
          },
          "description1": "车道中心线横向距离分布期望、车道中心线横向距离分布标准差、车道中心线横向距离分布最小值和横向相对位置震荡极差指标表现良好，平均值在合理范围内且不存在不合格用例；\n车道偏离漏预警次数、与近侧车道线的横向距离、车道中心线横向距离分布最大值和横向相对位置震荡频率指标平均值在合理范围外，算法在大部分用例下均表现不佳，需重点优化。",
          "description2": "经计算可知，车道偏离漏预警次数指标位于合理区间的占比为0.0%，与近侧车道线的横向距离指标位于合理区间的占比为0.0%，车道中心线横向距离分布期望指标位于合理区间的占比为100.0%，车道中心线横向距离分布标准差指标位于合理区间的占比为100.0%，车道中心线横向距离分布最大值指标位于合理区间的占比为0.0%，车道中心线横向距离分布最小值指标位于合理区间的占比为100.0%，横向相对位置震荡频率指标位于合理区间的占比为0.0%，横向相对位置震荡极差指标位于合理区间的占比为100.0%。"
        },
        "functionLDW": {
          "name": "LDW",
          "score": 10.0,
          "level": "较差",
          "gradeDistribution": {
            "优秀": 0,
            "良好": 0,
            "一般": 0,
            "较差": 100
          },
          "indexes": {
            "ldw_miss_warning_count3": {
              "name": "车道偏离漏预警次数3(yy)",
              "average": 9.61,
              "max": 9.61,
              "min": 9.61,
              "range": "[0, 1.0]"
            }
          },
          "builtin": {},
          "custom": {
            "ldw_miss_warning_count3": {
              "name": "ldw_miss_warning_count3",
              "data": [
                9.61,
                9.61
              ],
              "markLine": [
                1.0
              ]
            }
          },
          "description1": "车道偏离漏预警次数3指标平均值在合理范围外，算法在大部分用例下均表现不佳，需重点优化。",
          "description2": "经计算可知，车道偏离漏预警次数3指标位于合理区间的占比为0.0%。"
        }
      },
      "description1": "未满足设计指标要求。其中有2个用例得分低于60分，占比为100.00%，需优化算法在ACC、LKA和LDW上的表现；",
      "description2": "算法在ACC、LKA和LDW功能上需要重点优化。"
    },
    "compliance": {
      "name": "合规性",
      "weight": "20.0%",
      "score": 100.0,
      "level": "优秀",
      "scoreList": [
        100,
        100
      ],
      "levelDistribution": {
        "优秀": 100,
        "良好": 0,
        "一般": 0,
        "较差": 0
      },
      "details": {
        "deduct1": {
          "name": "轻微违规（扣1分）",
          "score": 100.0,
          "level": "优秀",
          "indexes": {
            "overspeed10": {
              "name": "超速10%以下",
              "times": 0
            },
            "overspeed10_20": {
              "name": "超速10%-20%",
              "times": 0
            }
          }
        },
        "deduct3": {
          "name": "中等违规（扣3分）",
          "score": 100.0,
          "level": "优秀",
          "indexes": {
            "pressSolidLine": {
              "name": "压实线",
              "times": 0
            }
          }
        },
        "deduct6": {
          "name": "危险违规（扣6分）",
          "score": 100.0,
          "level": "优秀",
          "indexes": {
            "runRedLight": {
              "name": "闯红灯",
              "times": 0
            },
            "overspeed20_50": {
              "name": "超速20%-50%",
              "times": 0
            }
          }
        },
        "deduct12": {
          "name": "重大违规（扣12分）",
          "score": 100.0,
          "level": "优秀",
          "indexes": {
            "overspeed50": {
              "name": "超速50%以上",
              "times": 0
            }
          }
        }
      },
      "description1": "车辆在本轮测试中无违反交通法规行为；",
      "description2": "共有2个用例，其中0个用例出现违规行为。"
    },
    "comfort": {
      "name": "舒适性",
      "weight": "10.0%",
      "score": 91.66,
      "level": "优秀",
      "scoreList": [
        91.66,
        91.66
      ],
      "levelDistribution": {
        "优秀": 100,
        "良好": 0,
        "一般": 0,
        "较差": 0
      },
      "details": {
        "comfortLat": {
          "name": "横向舒适性",
          "score": 93.33,
          "level": "优秀",
          "gradeDistribution": {
            "优秀": 100,
            "良好": 0,
            "一般": 0,
            "较差": 0
          },
          "indexes": {
            "zigzag": {
              "name": "画龙",
              "avgScore": 33.33,
              "maxScore": 33.33,
              "minScore": 33.33,
              "avgNumber": "1.00",
              "avgDuration": "3.40",
              "avgStrength": "0.00"
            },
            "shake": {
              "name": "晃动",
              "avgScore": 100.0,
              "maxScore": 100.0,
              "minScore": 100.0,
              "avgNumber": "0.00",
              "avgDuration": "0.00",
              "avgStrength": "0.00"
            }
          },
          "builtin": {
            "zigzag": {
              "name": "zigzag",
              "data": [
                33.33,
                33.33
              ]
            },
            "shake": {
              "name": "shake",
              "data": [
                100.0,
                100.0
              ]
            }
          },
          "custom": {},
          "description1": "晃动指标最低分超过设计指标要求，算法在2个用例中均表现良好；\n画龙指标平均分超过设计指标要求，但是算法存在2个表现不佳用例，需要改进算法在这些用例中的表现；。",
          "description2": "经计算可知，算法在画龙指标上表现良好的概率为0.0%，在晃动指标上表现良好的概率为100.0%。"
        },
        "comfortLon": {
          "name": "纵向舒适性",
          "score": 90.0,
          "level": "优秀",
          "gradeDistribution": {
            "优秀": 100,
            "良好": 0,
            "一般": 0,
            "较差": 0
          },
          "indexes": {
            "cadence": {
              "name": "顿挫",
              "avgScore": 100.0,
              "maxScore": 100.0,
              "minScore": 100.0,
              "avgNumber": "0.00",
              "avgDuration": "0.00",
              "avgStrength": "0.00"
            },
            "slamAccelerate": {
              "name": "急加速",
              "avgScore": 100.0,
              "maxScore": 100.0,
              "minScore": 100.0,
              "avgNumber": "0.00",
              "avgDuration": "0.00",
              "avgStrength": "0.00"
            }
          },
          "builtin": {
            "cadence": {
              "name": "cadence",
              "data": [
                100.0,
                100.0
              ]
            },
            "slamAccelerate": {
              "name": "slamAccelerate",
              "data": [
                100.0,
                100.0
              ]
            }
          },
          "custom": {},
          "description1": "顿挫和急加速指标最低分超过设计指标要求，算法在2个用例中均表现良好；。",
          "description2": "经计算可知，算法在顿挫指标上表现良好的概率为100.0%，在急加速指标上表现良好的概率为100.0%。"
        }
      },
      "description1": "乘客在本轮测试中体验舒适；",
      "description2": "算法在舒适性维度上的表现满足设计指标要求"
    },
    "efficient": {
      "name": "高效性",
      "weight": "20.0%",
      "score": 50.0,
      "level": "较差",
      "scoreList": [
        50,
        50
      ],
      "levelDistribution": {
        "优秀": 0,
        "良好": 0,
        "一般": 0,
        "较差": 100
      },
      "details": {
        "efficientDrive": {
          "name": "行驶",
          "score": 0.0,
          "level": "较差",
          "gradeDistribution": {
            "优秀": 0,
            "良好": 0,
            "一般": 0,
            "较差": 100
          },
          "indexes": {
            "averageSpeed": {
              "name": "平均速度(yy)",
              "average": 42.97,
              "max": 42.97,
              "min": 42.97,
              "number": 2,
              "range": "[0.0, 30.0]"
            }
          },
          "builtin": {
            "averageSpeed": {
              "name": "averageSpeed",
              "data": [
                42.97,
                42.97
              ],
              "markLine": [
                30.0
              ]
            }
          },
          "custom": {},
          "description1": "平均速度指标平均值在合理范围外，算法在大部分用例下均表现不佳，需重点优化。"
        },
        "efficientStop": {
          "name": "停车",
          "score": 100.0,
          "level": "优秀",
          "gradeDistribution": {
            "优秀": 100,
            "良好": 0,
            "一般": 0,
            "较差": 0
          },
          "indexes": {
            "stopDuration": {
              "name": "停车时长(yy)",
              "average": 0.0,
              "max": 0.0,
              "min": 0.0,
              "number": 0,
              "range": "[0, 5.0]"
            }
          },
          "builtin": {
            "stopDuration": {
              "name": "stopDuration",
              "data": [
                0.0,
                0.0
              ],
              "markLine": [
                5.0
              ]
            }
          },
          "custom": {},
          "description1": "停车时长指标表现良好，平均值在合理范围内且不存在不合格用例；。"
        }
      },
      "description1": "未满足设计指标要求。其中有2个用例得分低于60分，占比为100.00%，需优化算法在averageSpeed上的表现；",
      "description2": "算法应该优化车辆的规划控制逻辑，提高算法的通行效率。"
    },
    "customTest": {
      "name": "自定义x维度",
      "weight": "10.0%",
      "score": 100.0,
      "level": "优秀",
      "scoreList": [
        100,
        100
      ],
      "levelDistribution": {
        "优秀": 100,
        "良好": 0,
        "一般": 0,
        "较差": 0
      },
      "details": {
        "customType": {
          "name": "自定义x类型",
          "score": 100.0,
          "level": "优秀",
          "gradeDistribution": {
            "优秀": 100,
            "良好": 0,
            "一般": 0,
            "较差": 0
          },
          "indexes": {
            "ldw_miss_warning_count4": {
              "name": "车道偏离漏预警次数4(yy)",
              "average": 9.61,
              "max": 9.61,
              "min": 9.61,
              "range": "[0, 1.0]"
            },
            "ldw_miss_warning_count2": {
              "name": "车道偏离漏预警次数2(yy)",
              "average": 9.61,
              "max": 9.61,
              "min": 9.61,
              "range": "[0, 1.0]"
            }
          },
          "builtin": {},
          "custom": {
            "ldw_miss_warning_count4": {
              "name": "ldw_miss_warning_count4",
              "data": [
                9.61,
                9.61
              ],
              "markLine": [
                1.0
              ]
            },
            "ldw_miss_warning_count2": {
              "name": "ldw_miss_warning_count2",
              "data": [
                9.61,
                9.61
              ],
              "markLine": [
                1.0
              ]
            }
          },
          "description1": "车道偏离漏预警次数4和车道偏离漏预警次数2指标平均值在合理范围外，算法在大部分用例下均表现不佳，需重点优化。"
        }
      },
      "description1": "算法在本轮测试中的表现优秀；",
      "description2": "算法在自定义x维度维度上的表现满足设计指标要求。"
    }
  },
  "algorithmResultDescription": "综上所述，建议算法优化在安全性、功能性和高效性指标上的表现。"
}