How car camera applications can help us understand the cost differences for endpoint, edge or cloud AI/compute

Vision is different

Today, virtually every sensor and component in the car is “connected”, meaning sent outside the car. An awful lot of telemetry data is becoming available, from car data attributes that specify location, engine status, speed, maintenance, and even if a door was locked or the car refueled. This data comes from the vehicle’s electronic control units (ECUs), Controller Access Networks (CANs), and even infotainment systems. Vision data, from the various cameras in the car can provide far better “sensing”, creating the eyes on the road that drivers need for better driving, safety and security.

A free parking spot app indicating the location, age and visuals of a parking spot

Edge AI can and will enable vision

So, if transporting car camera vision data and running AI on it are essential, how can this cost barrier be overcome? The answer lies in edge AI — the ability to run efficient AI models at the network edge, such as 5G networks, or on the device/vehicle endpoint itself.

  • Model complexity: is the model simpler (such as object detection) or is it more complex, requiring a situational understanding or multi sensor aggregation? The more complex the model, the less likely it is to run on the endpoint.
  • The connectivity budget: is it restricted (less than 0.5 Gbs per month), unlimited or somewhere in between? This impacts the selectivity of data transfer — what type of data cherry picking occurs from the device to the cloud.
  • The latency tolerance of the specific application: is it mission critical (such as a safety alert based on crowd-sourced vision) or not (for instance an image of the street for mapping purposes), or something in between such as a free parking spot.

Putting a $$ value to the different compute options

Can you really put a dollar value to the different options: edge, endpoint and cloud compute?

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