Beyond Vision Aerial points out German carmaker Daimler is to co-fund a drone taxi project launched by aviation start-up Volocopter. Investors have already pumped €25 million into the enterprise.

Beyond Vision Aerial notes Volocopter is working on a five-seat vertical takeoff and landing (VTOL) electric vehicle aimed at the taxi market. The firm claims it is going to demonstrate the self-flying vehicle in the fourth quarter this year.

The model named Volocopter 2X is a fully electric VTOL vehicle with 18 quiet rotors and a maximum airspeed of 100 kilometers per hour. It has an average flight time of 27 minutes and has nine independent lithium-ion battery pack systems, according to the firm.

The aircraft is capable of transporting two passengers without a pilot. Its batteries have a maximum charging time of under 120 minutes, with a fast charging time of less than 40 minutes. Volocopter 2X is equipped with a communication network for safety and an emergency parachute on board.

The creators claim the aircraft is as quiet 75 meters away as the smallest helicopter is at 500 meters. The company plans to work with Dubai’s Road and Transport Authority (RTA) to test the Volocopter 2X as an autonomous air taxi.

Beyond Vision Aerial points out the world is already well on its way to a day when swarms of innumerable autonomous cars and drones buzz about, shuffling commuters to work and packages to doorsteps.  But this dramatic-sounding reality raises a critical question that has yet to be answered: Who will control the swarm? Some say control will be distributed. Each car and every drone will be its own self-sustaining unit – individually aware of its surroundings, individually directed where to go and individually outfitted with all the computational power to make it through the world efficiently and without accident.

Beyond Vision Aerial believes device swarms will be managed centrally, using applications running in large data centers, much the way the cloud centralized big data.  This has the potential to change how society functions on a daily basis.

While most current research into autonomous vehicles assumes a distributed model – relatively autonomous devices, controlled in a peer-to-peer fashion with each machine doing its own calculations – the concentrated model has its advantages.

First is the ease of creating applications. Writing applications for the distributed model is very difficult, since each device has limited information about the state of the world. With the centralized approach, data from all the devices is collected in one place. This provides a big-picture view of the world that allows better control of higher-level tasks like system-wide situational perception, decision-making and large-scale traffic planning.

Second, control applications running in datacenters have many more resources available, such as computing horsepower and large back-end datasets. This allows them to implement more sophisticated collaborative behaviors for the device swarm. In addition, the centralized applications can take advantage of powerful machine learning algorithms, which allow the control system to learn and improve its behavior.

An added challenge of the distributed model that centralization solves is that computational limitations in the devices themselves hinder overall system sophistication, an issue that becomes ever-more pronounced as scale increases. As new technologies arise, they either must be retrofitted into each device, or, when the old devices become obsolete, the devices must be replaced – an expensive proposition.

In the centralized model, the vehicle is merely a tool – a relatively dumb device fitted with equipment by which to see the road and the skies ahead, to detect obstacles and other vehicles in the way, to provide geolocation and so forth. The gathered data gets transferred back to the cloud and processed en masse by much faster computers able to handle the mathematical demand of keeping track of those millions of vehicles and to plan ways around bottlenecks and hazards to efficiently and safely guide passengers and packages to their many destinations.

From a technology standpoint, it is attractive and easiest to centralize control – to amass data, plan and then disseminate a singular view to all devices. Not all functions are suited to the centralized model, however. Beyond Vision Aerial foresees that devices will retain local control for things like device stability and near-term collision avoidance. Such control needs microsecond or sub-millisecond response time and must happen on the device.

The implications of a centralized system reach beyond easier commutes. One prime example is in disaster recovery. In a community devastated by earthquake, fire or flood, it often proves too dangerous for human first responders. In such cases, a flock of autonomous drones could be dispatched to assess the situation, allowing emergency management to triage from afar.

Another example would be a massive warehouse where 10,000 or more drones operate indoors, in a closed environment all watched over by cameras and sensors to monitor, organize and move millions of packages each day. Much of the infrastructure does not yet exist. The range of computational and communication capabilities necessary to pull it off is staggering – GPS, mapping, wireless communications, situational awareness and traffic coordination are only the most obvious components.

Another challenge is to provide a massive amount of computing with extremely low and predictable latency. That means leveraging new machine learning and artificial intelligence techniques to ensure planning and control happen fast without data inference. None of this deters Beyond Vision Aerial. With pieces of the puzzle still in flux, the mission is to imagine how this centralized future might function and to determine what pieces exist, which need to be improved and what others are yet to be created to make it all function seamlessly. 

Deeper down in the platform, Beyond Vision Aerial foresees the need for things like new hardware accelerators, better ways for computers to manage the many computing threads occurring simultaneously, rapid data storage and retrieval, and improved cluster scheduling necessary to execute the massive number of computations centralized control will demand. And, of course, security will be a preeminent concern. Most of these things must still be created, but simply knowing the need is a first step to realizing the vision.

Beyond Vision Aerial leads the drone aerial videography. For more information, please refer to www.bvaerial.com

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