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Saying AWS IoT FleetWise imaginative and prescient system information (Preview)


At this time, we’re excited to announce that AWS IoT FleetWise now helps car imaginative and prescient system information assortment that permits clients to gather metadata, object listing and detection information, and pictures or movies from digital camera, lidar, radar and different imaginative and prescient sub-systems. This new characteristic, now obtainable in Preview, builds upon present AWS IoT FleetWise capabilities that allow clients to extract extra worth and context from their information to construct automobiles which can be extra linked and handy.

Fashionable automobiles are geared up with a number of imaginative and prescient programs. Examples of imaginative and prescient programs embody a encompass view array of cameras and radars that allow superior driver help (ADAS) use circumstances and driver and cabin monitoring programs to help with driver consideration in semi-autonomous driving use circumstances. Most of those programs carry out some degree of computation on the car, usually utilizing refined algorithms for sensor fusion and AI/ML for inference.

Imaginative and prescient programs generate large quantities of knowledge in structured (numbers, textual content) and unstructured (photos, video) codecs. This problem makes it tough to synchronize information from a number of car sensor modalities round a given occasion of curiosity in a approach that minimizes interference with the operation of the car. For instance, to investigate the accuracy of highway situations detected by a car digital camera, a knowledge scientist could wish to view telemetry information (e.g., pace and brake strain), structured object lists and metadata, and unstructured photos/video information. Conserving all of these information factors organized and related to the identical occasion is a heavy carry. This usually requires extra software program and compute energy to solely gather information factors of curiosity to reduce interference with the operation of the car, add metadata, and preserve the info synchronized.

Imaginative and prescient system information from AWS IoT FleetWise lets automotive corporations simply gather and manage information from car imaginative and prescient programs that embody cameras, radars, and lidars. It retains each structured and unstructured imaginative and prescient system information, metadata, and telemetry information synchronized within the cloud, making it simpler for purchasers to assemble a full image view of occasions and achieve insights. Listed here are just a few situations:

  • To grasp what occurred throughout a hard-braking occasion, a buyer needs to gather information earlier than and after the occasion happens. The information collected could embody inference (e.g., an impediment was detected), timestamps and digital camera settings (metadata), and what occurred across the car (e.g., photos, movies, and lightweight/radar maps with bounding containers and detection overlays).
  • A buyer is concerned with anomalous occasions on roadways like accidents, wildfires, and obstacles that impede visitors. The client begins by gathering telemetry and object listing information at scale throughout numerous automobiles, then, zooms in on a set of automobiles which can be signaling anomalous occasions (e.g., pace is 0 on a big freeway) and collects imaginative and prescient system information from these automobiles.

When gathering imaginative and prescient system information utilizing AWS IoT FleetWise, clients can benefit from the service’s superior options and interfaces they already use to gather telemetry information, for instance, specifying occasions of their information assortment marketing campaign to optimize bandwidth and information measurement. Clients can get began on AWS by defining and modeling a car’s imaginative and prescient system, alongside its attributes and telemetry sensors. The client’s Edge Agent deployed within the car collects information from CAN-based car sensors (e.g. battery temperature), in addition to from car sub-systems that embody imaginative and prescient system sensors. Clients can use the identical event- or time-based information assortment marketing campaign to gather information alerts concurrently from each commonplace sensors and imaginative and prescient programs. Within the cloud, clients see a unified view of their outlined car attributes and different metadata, telemetry information, and structured imaginative and prescient system information, with hyperlinks to view unstructured imaginative and prescient system information in Amazon Easy Storage Service (Amazon S3). The information stays synchronized utilizing car, marketing campaign, and occasion identifiers. Clients can then use companies like AWS Glue to combine information for downstream analytics.

Continental AG is growing driver comfort options

Continental AG develops pioneering applied sciences and companies for autonomous mobility. “Continental has collaborated carefully with AWS on growing applied sciences that speed up automotive software program improvement within the cloud. With imaginative and prescient system information from AWS IoT FleetWise, we can simply gather digital camera and motion-planning information to enhance automated parking help and allow fleet-wide monitoring and reporting.”

Yann Baudouin, Head of Knowledge Options – Engineering Platform and Ecosystem, Continental AG

HL Mando is growing capabilities that improve driver security and personalization

HL Mando is a tier 1 provider of components and software program to the automotive trade. “At Mando, we’re dedicated to innovating know-how that makes automobiles simpler to drive and function. Our options depend on the power to gather car telemetry information in addition to car digital camera information in an environment friendly approach. We’re trying ahead to utilizing the info we gather by way of AWS IoT FleetWise to enhance car software program capabilities that may improve driver security and driver personalization.” 

Seong-Hyeon Cho, Vice Chairman/CEO, HL Mando

ThunderSoft is growing automotive and fleet options

ThunderSoft gives clever working programs and applied sciences to automotive corporations and enterprises. “As ThunderSoft works to assist advance the subsequent technology of linked car know-how throughout the globe, we sit up for persevering with our collaboration with AWS. With the arrival of imaginative and prescient system information from AWS IoT FleetWise, we’ll be capable of assist our clients with modern options for superior driver help programs (ADAS) and fleet administration.”

Pengcheng Zou, CTO, ThunderSoft

Answer Overview

Let’s take an ADAS use case to stroll by way of the method of gathering imaginative and prescient system information. Think about that an ADAS engineer is deploying a collision avoidance system in manufacturing automobiles. A technique this technique helps automobiles keep away from collisions is by mechanically making use of brakes in sure situations (e.g., an impending rear-end collision with one other car).

Whereas the software program used on this system has already gone by way of rigorous testing, the engineer needs to constantly enhance the software program for each current-gen and future-gen automobiles. On this case, the engineer needs to see all situations the place a collision was detected. To grasp what occurred throughout the occasion, the engineer will take a look at imaginative and prescient information comprised of photos and telemetry information earlier than and after the collision was detected. As soon as within the S3 bucket, the engineer could wish to visualize, analyze and label the info.

Stipulations

Earlier than you get began, you’ll need:

  • An AWS account with console, CLI and programmatic entry in supported Areas.
  • Permission to create and entry AWS IoT FleetWise and Amazon S3 sources.
  • To comply with the directions in our AWS IoT FleetWise imaginative and prescient system demo information, as much as and together with, “Playback ROS 2 information.”
  • (Optionally available) A ROS 2 setting that helps the “Galactic” model of ROS 2. Throughout the Preview interval for imaginative and prescient system information, the AWS IoT FleetWise Reference Edge Agent helps ROS 2 middleware to gather imaginative and prescient system alerts.

Walkthrough

Step 1: Mannequin your car

  • Create a sign catalog by creating the file: ros2-nodes.json . Be happy to alter the title and outline inside this file to your liking.
{
 "title": "fw-vision-system-catalog",
    "description": "vision-system-catalog",
    "nodes": [
      {
        "branch": {
          "fullyQualifiedName": "Types"
        }
      },
      {
        "struct": {
          "fullyQualifiedName": "Types.sensor_msgs_msg_CompressedImage"
        }
      },
      {
        "struct": {
          "fullyQualifiedName": "Types.std_msgs_Header"
        }
      },
      {
        "struct": {
          "fullyQualifiedName": "Types.builtin_interfaces_Time"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.builtin_interfaces_Time.sec",
          "dataType": "INT32",
          "dataEncoding": "TYPED"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.builtin_interfaces_Time.nanosec",
          "dataType": "UINT32",
          "dataEncoding": "TYPED"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.std_msgs_Header.stamp",
          "dataType": "STRUCT",
          "structFullyQualifiedName": "Types.builtin_interfaces_Time"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.std_msgs_Header.frame_id",
          "dataType": "STRING",
          "dataEncoding": "TYPED"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.sensor_msgs_msg_CompressedImage.header",
          "dataType": "STRUCT",
          "structFullyQualifiedName": "Types.std_msgs_Header"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.sensor_msgs_msg_CompressedImage.format",
          "dataType": "STRING",
          "dataEncoding": "TYPED"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.sensor_msgs_msg_CompressedImage.data",
          "dataType": "UINT8_ARRAY",
          "dataEncoding": "BINARY"
        }
      },
      {
        "branch": {
          "fullyQualifiedName": "Vehicle",
          "description": "Vehicle"
        }
      },
      {
        "branch": {
          "fullyQualifiedName": "Vehicle.Cameras",
          "description": "Vehicle.Cameras"
        }
      },
      {
        "branch": {
          "fullyQualifiedName": "Vehicle.Cameras.Front",
          "description": "Vehicle.Cameras.Front"
        }
      },
      {
        "sensor": {
          "fullyQualifiedName": "Vehicle.Cameras.Front.Image",
          "dataType": "STRUCT",
          "structFullyQualifiedName": "Types.sensor_msgs_msg_CompressedImage"
        }
      },
      {
        "struct": {
          "fullyQualifiedName": "Types.std_msgs_msg_Float32"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.std_msgs_msg_Float32.data",
          "dataType": "FLOAT",
          "dataEncoding": "TYPED"
        }
      },
      {
        "sensor": {
          "fullyQualifiedName": "Vehicle.Speed",
          "dataType": "STRUCT",
          "structFullyQualifiedName": "Types.std_msgs_msg_Float32"
        }
      },
      {
        "branch": {
          "fullyQualifiedName": "Vehicle.Airbag",
          "description": "Vehicle.Airbag"
        }
      },
      {
        "sensor": {
          "fullyQualifiedName": "Vehicle.Airbag.CollisionIntensity",
          "dataType": "STRUCT",
          "structFullyQualifiedName": "Types.std_msgs_msg_Float32"
        }
      },
      {
        "struct": {
          "fullyQualifiedName": "Types.sensor_msgs_msg_Imu"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.sensor_msgs_msg_Imu.header",
          "dataType": "STRUCT",
          "structFullyQualifiedName": "Types.std_msgs_Header"
        }
      },
      {
        "struct": {
          "fullyQualifiedName": "Types.geometry_msgs_Quaternion"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.geometry_msgs_Quaternion.x",
          "dataType": "DOUBLE",
          "dataEncoding": "TYPED"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.geometry_msgs_Quaternion.y",
          "dataType": "DOUBLE",
          "dataEncoding": "TYPED"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.geometry_msgs_Quaternion.z",
          "dataType": "DOUBLE",
          "dataEncoding": "TYPED"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.geometry_msgs_Quaternion.w",
          "dataType": "DOUBLE",
          "dataEncoding": "TYPED"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.sensor_msgs_msg_Imu.orientation",
          "dataType": "STRUCT",
          "structFullyQualifiedName": "Types.geometry_msgs_Quaternion"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.sensor_msgs_msg_Imu.orientation_covariance",
          "dataType": "DOUBLE_ARRAY",
          "dataEncoding": "TYPED"
        }
      },
      {
        "struct": {
          "fullyQualifiedName": "Types.geometry_msgs_Vector3"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.geometry_msgs_Vector3.x",
          "dataType": "DOUBLE",
          "dataEncoding": "TYPED"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.geometry_msgs_Vector3.y",
          "dataType": "DOUBLE",
          "dataEncoding": "TYPED"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.geometry_msgs_Vector3.z",
          "dataType": "DOUBLE",
          "dataEncoding": "TYPED"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.sensor_msgs_msg_Imu.angular_velocity",
          "dataType": "STRUCT",
          "structFullyQualifiedName": "Types.geometry_msgs_Vector3"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.sensor_msgs_msg_Imu.angular_velocity_covariance",
          "dataType": "DOUBLE_ARRAY",
          "dataEncoding": "TYPED"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.sensor_msgs_msg_Imu.linear_acceleration",
          "dataType": "STRUCT",
          "structFullyQualifiedName": "Types.geometry_msgs_Vector3"
        }
      },
      {
        "property": {
          "fullyQualifiedName": "Types.sensor_msgs_msg_Imu.linear_acceleration_covariance",
          "dataType": "DOUBLE_ARRAY",
          "dataEncoding": "TYPED"
        }
      },
      {
        "sensor": {
          "fullyQualifiedName": "Vehicle.Acceleration",
          "dataType": "STRUCT",
          "structFullyQualifiedName": "Types.sensor_msgs_msg_Imu"
        }
      }
    ]
}
aws iotfleetwise create-signal-catalog --cli-input-json file://ros2-nodes.json
  • AWS IoT FleetWise can gather each imaginative and prescient system and CAN bus information on the identical time. You may also replace the sign catalog by including CAN alerts from any vss-json file. Ensure that the “title” discipline within the file matches the sign catalog you created:
aws iotfleetwise update-signal-catalog --cli-input-json file://<can-nodes>.json
  • Create a mannequin manifest named: vehicle-model.json. Your mannequin manifest needs to be comprised of the next alerts (absolutely certified names outlined under):
    • Automobile.Cameras.Entrance.Picture
    • Automobile.Pace
    • Automobile.Acceleration
    • Automobile.Airbag.CollisionIntensity
{

"title": "fw-vision-system-model",

"signalCatalogArn": "<signal-catalog-ARN>",

"description": "Automobile mannequin to display FleetWise imaginative and prescient system information",

"nodes": ["Vehicle.Cameras.Front.Image","Vehicle.Speed","Vehicle.Airbag.CollisionIntensity","Vehicle.Acceleration"]

}
aws iotfleetwise create-model-manifest --cli-input-json file://vehicle-model.json
  • Replace your mannequin manifest by setting it to ‘energetic:’
aws iotfleetwise update-model-manifest --name fw-vision-system-model --status ACTIVE
  • Create a decoder manifest file: decoder-manifest.json. Modify the JSON to mirror the suitable mannequin manifest ARN. In case you’re additionally utilizing CAN alerts, seek advice from the AWS IoT FleetWise documentation for an instance decoder manifest with each imaginative and prescient system and CAN alerts. You have to to replace the decoder manifest to ‘energetic’ standing when you create the decoder manifest:
{
    "title": "fw-vision-system-decoder-manifest",
    "modelManifestArn": "<your mannequin manifest arn>",
    "description": "decoder manifest to display imaginative and prescient system information",
    "networkInterfaces":[
  {
    "interfaceId": "10",
    "type": "VEHICLE_MIDDLEWARE",
    "vehicleMiddleware": {
      "name": "ros2",
      "protocolName": "ROS_2"
    }
  },
],

"signalDecoders":[	
  {
    "fullyQualifiedName": "Vehicle.Cameras.Front.Image",
    "type": "MESSAGE_SIGNAL",
    "interfaceId": "10",
    "messageSignal": {
      "topicName": "/carla/ego_vehicle/rgb_front/image_compressed:sensor_msgs/msg/CompressedImage",
      "structuredMessage": {
        "structuredMessageDefinition": [
          {
            "fieldName": "header",
            "dataType": {
              "structuredMessageDefinition": [
                {
                  "fieldName": "stamp",
                  "dataType": {
                    "structuredMessageDefinition": [
                      {
                        "fieldName": "sec",
                        "dataType": {
                          "primitiveMessageDefinition": {
                            "ros2PrimitiveMessageDefinition": {
                              "primitiveType": "INT32"
                            }
                          }
                        }
                      },
                      {
                        "fieldName": "nanosec",
                        "dataType": {
                          "primitiveMessageDefinition": {
                            "ros2PrimitiveMessageDefinition": {
                              "primitiveType": "UINT32"
                            }
                          }
                        }
                      }
                    ]
                  }
                },
                {
                  "fieldName": "frame_id",
                  "dataType": {
                    "primitiveMessageDefinition": {
                      "ros2PrimitiveMessageDefinition": {
                        "primitiveType": "STRING"
                      }
                    }
                  }
                }
              ]
            }
          },
          {
            "fieldName": "format",
            "dataType": {
              "primitiveMessageDefinition": {
                "ros2PrimitiveMessageDefinition": {
                  "primitiveType": "STRING"
                }
              }
            }
          },
          {
            "fieldName": "information",
            "dataType": {
              "structuredMessageListDefinition": {
                "title": "listType",
                "memberType": {
                  "primitiveMessageDefinition": {
                    "ros2PrimitiveMessageDefinition": {
                      "primitiveType": "UINT8"
                    }
                  }
                },
                "capability": 0,
                "listType": "DYNAMIC_UNBOUNDED_CAPACITY"
              }
            }
          }
        ]
      }
    }
  },
  {
    "fullyQualifiedName": "Automobile.Pace",
    "kind": "MESSAGE_SIGNAL",
    "interfaceId": "10",
    "messageSignal": {
      "topicName": "/carla/ego_vehicle/speedometer:std_msgs/msg/Float32",
      "structuredMessage": {
        "structuredMessageDefinition": [
          {
            "fieldName": "data",
            "dataType": {
              "primitiveMessageDefinition": {
                "ros2PrimitiveMessageDefinition": {
                  "primitiveType": "FLOAT32"
                }
              }
            }
          }
        ]
      }
    }
  },
  {
    "fullyQualifiedName": "Automobile.Airbag.CollisionIntensity",
    "kind": "MESSAGE_SIGNAL",
    "interfaceId": "10",
    "messageSignal": {
      "topicName": "/carla/ego_vehicle/collision_intensity:std_msgs/msg/Float32",
      "structuredMessage": {
        "structuredMessageDefinition": [
          {
            "fieldName": "data",
            "dataType": {
              "primitiveMessageDefinition": {
                "ros2PrimitiveMessageDefinition": {
                  "primitiveType": "FLOAT32"
                }
              }
            }
          }
        ]
      }
    }
  },
  {
    "fullyQualifiedName": "Automobile.Acceleration",
    "kind": "MESSAGE_SIGNAL",
    "interfaceId": "10",
    "messageSignal": {
      "topicName": "/carla/ego_vehicle/imu:sensor_msgs/msg/Imu",
      "structuredMessage": {
        "structuredMessageDefinition": [
          {
            "fieldName": "header",
            "dataType": {
              "structuredMessageDefinition": [
                {
                  "fieldName": "stamp",
                  "dataType": {
                    "structuredMessageDefinition": [
                      {
                        "fieldName": "sec",
                        "dataType": {
                          "primitiveMessageDefinition": {
                            "ros2PrimitiveMessageDefinition": {
                              "primitiveType": "INT32"
                            }
                          }
                        }
                      },
                      {
                        "fieldName": "nanosec",
                        "dataType": {
                          "primitiveMessageDefinition": {
                            "ros2PrimitiveMessageDefinition": {
                              "primitiveType": "UINT32"
                            }
                          }
                        }
                      }
                    ]
                  }
                },
                {
                  "fieldName": "frame_id",
                  "dataType": {
                    "primitiveMessageDefinition": {
                      "ros2PrimitiveMessageDefinition": {
                        "primitiveType": "STRING"
                      }
                    }
                  }
                }
              ]
            }
          },
          {
            "fieldName": "orientation",
            "dataType": {
              "structuredMessageDefinition": [
                {
                  "fieldName": "x",
                  "dataType": {
                    "primitiveMessageDefinition": {
                      "ros2PrimitiveMessageDefinition": {
                        "primitiveType": "FLOAT64"
                      }
                    }
                  }
                },
                {
                  "fieldName": "y",
                  "dataType": {
                    "primitiveMessageDefinition": {
                      "ros2PrimitiveMessageDefinition": {
                        "primitiveType": "FLOAT64"
                      }
                    }
                  }
                },
                {
                  "fieldName": "z",
                  "dataType": {
                    "primitiveMessageDefinition": {
                      "ros2PrimitiveMessageDefinition": {
                        "primitiveType": "FLOAT64"
                      }
                    }
                  }
                },
                {
                  "fieldName": "w",
                  "dataType": {
                    "primitiveMessageDefinition": {
                      "ros2PrimitiveMessageDefinition": {
                        "primitiveType": "FLOAT64"
                      }
                    }
                  }
                }
              ]
            }
          },
          {
            "fieldName": "orientation_covariance",
            "dataType": {
              "structuredMessageListDefinition": {
                "title": "listType",
                "memberType": {
                  "primitiveMessageDefinition": {
                    "ros2PrimitiveMessageDefinition": {
                      "primitiveType": "FLOAT64"
                    }
                  }
                },
                "capability": 9,
                "listType": "FIXED_CAPACITY"
              }
            }
          },
          {
            "fieldName": "angular_velocity",
            "dataType": {
              "structuredMessageDefinition": [
                {
                  "fieldName": "x",
                  "dataType": {
                    "primitiveMessageDefinition": {
                      "ros2PrimitiveMessageDefinition": {
                        "primitiveType": "FLOAT64"
                      }
                    }
                  }
                },
                {
                  "fieldName": "y",
                  "dataType": {
                    "primitiveMessageDefinition": {
                      "ros2PrimitiveMessageDefinition": {
                        "primitiveType": "FLOAT64"
                      }
                    }
                  }
                },
                {
                  "fieldName": "z",
                  "dataType": {
                    "primitiveMessageDefinition": {
                      "ros2PrimitiveMessageDefinition": {
                        "primitiveType": "FLOAT64"
                      }
                    }
                  }
                }
              ]
            }
          },
          {
            "fieldName": "angular_velocity_covariance",
            "dataType": {
              "structuredMessageListDefinition": {
                "title": "listType",
                "memberType": {
                  "primitiveMessageDefinition": {
                    "ros2PrimitiveMessageDefinition": {
                      "primitiveType": "FLOAT64"
                    }
                  }
                },
                "capability": 9,
                "listType": "FIXED_CAPACITY"
              }
            }
          },
          {
            "fieldName": "linear_acceleration",
            "dataType": {
              "structuredMessageDefinition": [
                {
                  "fieldName": "x",
                  "dataType": {
                    "primitiveMessageDefinition": {
                      "ros2PrimitiveMessageDefinition": {
                        "primitiveType": "FLOAT64"
                      }
                    }
                  }
                },
                {
                  "fieldName": "y",
                  "dataType": {
                    "primitiveMessageDefinition": {
                      "ros2PrimitiveMessageDefinition": {
                        "primitiveType": "FLOAT64"
                      }
                    }
                  }
                },
                {
                  "fieldName": "z",
                  "dataType": {
                    "primitiveMessageDefinition": {
                      "ros2PrimitiveMessageDefinition": {
                        "primitiveType": "FLOAT64"
                      }
                    }
                  }
                }
              ]
            }
          },
          {
            "fieldName": "linear_acceleration_covariance",
            "dataType": {
              "structuredMessageListDefinition": {
                "title": "listType",
                "memberType": {
                  "primitiveMessageDefinition": {
                    "ros2PrimitiveMessageDefinition": {
                      "primitiveType": "FLOAT64"
                    }
                  }
                },
                "capability": 9,
                "listType": "FIXED_CAPACITY"
              }
            }
          }
        ]
      }
    }
  }
]
}
aws iotfleetwise create-decoder-manifest --cli-input-json file://decoder-manifest.json

aws iotfleetwise update-decoder-manifest —title fw-vision-system-decoder-manifest —standing ACTIVE

Step 2: Create a car

  • Create a car utilizing the above mannequin manifest and decoder manifest. Be sure you use the identical title because the provisioned AWS IoT Factor that you simply created in your prerequisite steps.
aws iotfleetwise create-vehicle --vehicle-name FW-VSD-ROS2-<provisioned-identifier>-vehicle --model-manifest-arn <Your mannequin manifest ARN> --decoder-manifest-arn <Your decoder manifest ARN>

Step 3: Create campaigns

  • Arrange the entry coverage to allow AWS IoT FleetWise to entry your S3 bucket by following the directions right here (see “bucket coverage for all campaigns”)
  • Create an event-based marketing campaign that collects information primarily based on a detected collision occasion, together with 5 seconds of pretrigger and 5 seconds of posttrigger information.
{
    "title": "fw-vision-system-collectCollision",
    "description": "Accumulate 10 seconds of knowledge from a subset of alerts if car detected a collision - 5 pretrigger seconds, 5 posttrigger seconds",
    "signalCatalogArn": "<your sign catalog>",
    "targetArn": "<your goal>",
        "signalsToCollect": [
        {
            "name": "Vehicle.Cameras.Front.Image",
            "maxSampleCount": 1000,
            "minimumSamplingIntervalMs": 10
        },
        {
            "name": "Vehicle.Speed",
            "maxSampleCount": 1000,
            "minimumSamplingIntervalMs": 10
        },
        {
            "name": "Vehicle.Acceleration",
            "maxSampleCount": 1000,
            "minimumSamplingIntervalMs": 10
        },
        {
            "name": "Vehicle.Airbag.CollisionIntensity",
            "maxSampleCount": 1000,
            "minimumSamplingIntervalMs": 10
        }
    ],
    "postTriggerCollectionDuration": 5000,
    "collectionScheme": {
        "conditionBasedCollectionScheme": {
            "conditionLanguageVersion": 1,
            "expression": "$variable.`Automobile.Airbag.CollisionIntensity` > 1",
            "minimumTriggerIntervalMs": 10000,
            "triggerMode": "ALWAYS"
        }
    },
    "dataDestinationConfigs": [
        {
            "s3Config": {
                "bucketArn": "<your S3 bucket>",
                "dataFormat": "PARQUET",
                "storageCompressionFormat": "NONE",
                "prefix": "collisionData"
            }
        }
    ]
}
aws iotfleetwise create-campaign --cli-input-json file://marketing campaign.json
  • Create one other marketing campaign to gather 10 seconds of knowledge as a timed occasion.
{
    "title": "fw-vision-system-collectTimed",
    "description": "Accumulate 10 seconds of knowledge from a subset of alerts",
    "signalCatalogArn": "<Your sign catalog ARN>",
    "targetArn": "<Your car ARN>",
        "signalsToCollect": [
        {
            "name": "Vehicle.Cameras.Front.Image",
            "maxSampleCount": 500,
            "minimumSamplingIntervalMs": 10
        },
        {
            "name": "Vehicle.Speed",
            "maxSampleCount": 500,
            "minimumSamplingIntervalMs": 10
        },
        {
            "name": "Vehicle.Acceleration",
            "maxSampleCount": 500,
            "minimumSamplingIntervalMs": 10
        },
        {
            "name": "Vehicle.Airbag.CollisionIntensity",
            "maxSampleCount": 500,
            "minimumSamplingIntervalMs": 10
        }
    ],
    "postTriggerCollectionDuration": 5000,
    "collectionScheme": {
        "timeBasedCollectionScheme": {
            "periodMs": 10000
        }
    },
    "dataDestinationConfigs": [
        {
            "s3Config": {
                "bucketArn": "<Your S3 bucket>",
                "dataFormat": "PARQUET",
                "storageCompressionFormat": "NONE",
                "prefix": "timeData"
            }
        }
    ]
}
aws iotfleetwise create-campaign --cli-input-json file://campaign-timed.json
  • Ensure that to approve all of your campaigns!
aws iotfleetwise update-campaign --name fw-rich-sensor-collectCollision --action APPROVE

aws iotfleetwise update-campaign --name fw-rich-sensor-collectTimed --action APPROVE

Step 4: View your information in Amazon S3 

AWS IoT FleetWise takes as much as quarter-hour to load your information into Amazon S3. You will note three units of information in your S3 bucket: 1/Uncooked information or iON information that incorporates the binary blobs of knowledge that AWS IoT FleetWise decodes — these information can be utilized to deep dive errors; 2/Unstructured information information that include binaries for photos/video collected; 3/Processed information (i.e., structured information) information that include decoded metadata, object lists and telemetry information, with hyperlinks to corresponding unstructured information information.

To do extra, you possibly can:

  • Make the most of marketing campaign ID, occasion ID, and car ID to ‘be a part of’ your information utilizing AWS Glue.
  • Catalog your information utilizing an AWS Glue Crawler to make it searchable.

Discover your information utilizing ad-hoc queries in Amazon Athena to determine scenes of curiosity.

Knowledge from scenes of curiosity can then be handed to downstream instruments for visualization, labeling, and re-simulation to develop the subsequent model of fashions and car software program. For instance, third celebration software program similar to Foxglove Studio can be utilized to visualise what occurred earlier than and after the collision utilizing the photographs saved in Amazon S3; Amazon Rekognition may be utilized to mechanically uncover and label extra objects current on the time of collision; Amazon SageMaker Groundtruth can be utilized for annotation and human-in-the-loop workflows to enhance the accuracy and relevance of the collision avoidance software program. In a future weblog, we plan to discover choices for this a part of the workflow.

Conclusion 

On this submit, we showcased how AWS IoT FleetWise imaginative and prescient system information allows you to simply gather and manage information from superior car sensor programs to assemble a holistic view of occasions and achieve insights. The brand new characteristic expands the scope of data-driven use circumstances for automotive clients. We then used a pattern ADAS improvement use case to stroll by way of the method of making condition-based campaigns may help enhance an ADAS system, and learn how to entry that information in Amazon S3.

To study extra, go to the AWS IoT FleetWise website. We sit up for your suggestions and questions.

In regards to the Authors


Akshay Tandon
is a Principal Product Supervisor at Amazon Internet Companies with the AWS IoT FleetWise workforce. He’s obsessed with the whole lot automotive and product. He enjoys listening to clients and envisioning modern services and products that assist fulfill their wants. At Amazon, Akshay has led product initiatives within the AI/ML house with Alexa and the fleet administration house with Amazon Transportation Companies. He has greater than 10 years of product administration expertise.


Matt Pollock
is a Senior Answer Architect at Amazon Internet Companies presently working with automotive OEMs and suppliers. Based mostly in Austin, Texas, he has labored with clients on the interface of digital and bodily programs throughout a various vary of industries since 2005. When not constructing scalable options to difficult technical issues, he enjoys telling horrible jokes to his daughter.

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