Unlock the Power of Vision Analytics with Fleet Queries: A Guide to Optimizing Your ADAS Fleet

Advanced Driver Assistance Systems (ADAS) are becoming increasingly prevalent in today’s vehicles, and with that comes the need for efficient and effective data management. One powerful tool for managing data from a fleet of ADAS-equipped vehicles is the fleet query.

A fleet query is a command that is sent to a fleet of ADAS vehicles, instructing them to collect and transmit specific data. This data can then be used for various purposes, such as training machine learning models or identifying patterns in vehicle behavior.

One way to send a fleet query is through a YAML file. An example of a fleet query YAML file might look like this:

query_name: "Collect lane departure data"
vehicle_id: "*" # Collect data from all vehicles in the fleet
start_time: "2022-01-01 00:00:00"
end_time: "2022-01-31 23:59:59"
data_types:
  - "camera"
  - "lidar"
  - "radar"
query_filters:
  - "lane_departure=True"

This fleet query would collect data from all vehicles in the fleet between January 1st and January 31st of 2022, including camera, lidar, and radar data. The query would also include a filter for only collecting data when a lane departure is detected.

Once the data is collected, it can be integrated into a fleet learning data pipeline for further analysis and processing. This data can then be used to train machine learning models to improve ADAS performance, or to identify patterns in vehicle behavior that can be used to improve safety and efficiency.

In short, fleet queries and vision analytics can help to unlock the full potential of your ADAS fleet by providing a powerful tool for collecting and analyzing data. With the ability to send specific commands to a fleet of vehicles and access to a wealth of data, fleet queries can help you to optimize your ADAS fleet and improve overall performance

Some more examples of fleet queries include:

Collect speed data from all vehicles in the fleet:

query_name: "Collect speed data"
vehicle_id: "*"
start_time: "2022-01-01 00:00:00"
end_time: "2022-01-31 23:59:59"
data_types:
  - "speed"
query_filters:
  - "speed>0"

Collect camera data from a specific vehicle:

query_name: "Collect camera data"
vehicle_id: "VIN1234567890"
start_time: "2022-01-01 00:00:00"
end_time: "2022-01-31 23:59:59"
data_types:
  - "camera"

Collect lidar data from all vehicles during nighttime hours:

query_name: "Collect nighttime lidar data"
vehicle_id: "*"
start_time: "2022-01-01 18:00:00"
end_time: "2022-01-31 06:00:00"
data_types:
  - "lidar"
query_filters:
  - "light_conditions=dark"

Collect radar data from all vehicles in a specific geographic location:

query_name: "Collect radar data in downtown area"
vehicle_id: "*"
start_time: "2022-01-01 00:00:00"
end_time: "2022-01-31 23:59:59"
data_types:
  - "radar"
query_filters:
  - "latitude>41.88"
  - "latitude<41.92"
  - "longitude>-87.63"
  - "longitude<-87.60"

Collect data from all sensors, filtered by specific events:

query_name: "Collect data on specific events"
vehicle_id: "*"
start_time: "2022-01-01 00:00:00"
end_time: "2022-01-31 23:59:59"
data_types:
  - "all"
query_filters:
  - "event=hard_brake"
  - "event=sharp_turn"

Note: The above examples are written in YAML, but the syntax for fleet queries can vary depending on the system being used, and the above code should be adapted accordingly.