The autonomous vehicles’ race has been hot for a while. However, there is one field, less heard of, whose companies collect data that brings tremendous value and paves the way for Autonomous Vehicles — the fleet management technology industry.
I will focus on three key components already covered within fleet management, and explain how they contribute to the future of AVs.
1 — Maintenance
For Autonomous vehicles, remote maintenance tools are crucial to enhance performance and safety on the road when there’s no driver to watch over the car.
- Refueling/Recharging (In the AV case — battery usage). For AVs, it’s essential to know how to plan the upcoming trips appropriately. The system knows, for example, that from Monday to Friday, you commute to the workplace, the same as human fleet managers know how to load balance their fleet based on different days of the week. Therefore, it can schedule battery charging according to actual and planned usage. Moreover, the system is equipped with GPS analytics and can identify map locations in which charging would occur at the right timing. As in current fleet management, this is all about maximizing the car’s utility to the owner, not necessarily maximizing road time.
- Route tracking: Any vehicle needs to plan which route to take based on various factors — some, as mentioned before, are related to maintenance, while others are related to road conditions. Fleet management tools can find vehicles and route history on a map with real-time GPS tracking. Point-by-point location information includes the date, time, altitude, speed, and any moments of risky driving behavior. This data matters because it helps us to forecast the road conditions and predict the next steps. For example, if most vehicles break instantly at a specific location, we can understand that there might be a hazard on that spot. This information can shed light on road structure/conditions and help optimize our fleet — either managing driving behavior in this specific road or trying to avoid this road overall.
- Kilometers remaining until scheduled service Whether it’s a personal AV or part of a fleet such as Uber, this aspect is important. The AV is a new kind of more complex vehicle that consumes much power to process and run algorithms. We should know how to maintain safety, and vehicle function at least as the way do now — which poses a challenge, having no driver who ‘feels’ the car is ‘a bit off.’ We can use the data collected from EV car fleets today to predict when is the next time the vehicle should get to the shop and ensure this does not have urgent business implications.
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2 — ADAS/Behavior
ADAS (Advanced Driver Assistance System) smart solutions take us one step closer to fully autonomous driving, not only by advancing safety but also by capturing data that could be used for training. Many fleets are equipped with camera vision (even 360 degrees), allowing to capture footage of the road ahead and the vehicle’s cabin to detect risk wherever it occurs.
For this feature, I think it’s pretty obvious to have both a camera in front of the vehicle, one behind it, and one within the cabin itself to monitor driver behavior.
These cameras’ data is perfect for AV as it captures precisely human behavior and reaction to road conditions as we humans see it.
The computer vision data can be used to “teach” AV models how we humans drive. Moreover, I think this type of data is needed for AVs to imitate the exact human behavior: people don’t have lidars or thermal cameras, just our eyes, and we get pretty good results — there’s much to learn from both how we drive, and how we REACT to road conditions even before we made a driving decision.
3 — AV fleet management
When you manage a fleet of AVs, you have much more flexibility as human limitations do not apply. However, you also lose the independent judgment of human drivers. Thus you have to issue very precise instructions.
Today’s tools allow you to get instant access to live video from the road, manage risky driving, and review recordings to investigate incidents at any time. By doing so, this feature allows AV fleet management to scale their capabilities and monitor the vehicles to perform better, and eventually make an optimized decision for the fleet.
For example, we can optimize scheduling for real-time driving requests like Uber or food shipment or fleet management for trucks. We already have all statistics: their ride, their schedule, their performance etc — now, we can actively manage the AV fleet using this data to optimize the entire fleet’s results.
Data has become the next currency and is valued more than ever (ask Google). Moreover, data is a big part of what we need to accelerate AV R&D. Fleet management technology exists today and produces a ton of data — a perfect combo.
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