Self-driving cars have conquered our imaginations, but they're having a bit more trouble with potholes. If they don't have the right information about a road's condition before they begin to use it, the best autonomous cars can do is rapidly apply the brake. Helpfully, explosions are irregular occurrences in civilian scenarios, and bridges don't appear and disappear from one day to the next. In the middle of a military operation, however, you might prefer not to have to stop and determine a new route.
At the same time, it's rare for a civilian road user to be directed in concert with others through a data-rich and fully featured command and control system, and even rarer for them to be continually feeding relevant information back into it. Given the array of high-resolution data sources and calculation abilities that can be incorporated into such a system, however, autonomous military vehicles should be embarrassing their civilian counterparts.
"The technology is not really used like this at the moment," says Tobias Moberg, head of product management at Carmenta Geospatial Technologies, "but both the data sources and the algorithms are good enough today that they can be used as an input for an autonomous vehicle."
"We can use terrain routing to determine how an autonomous or a manned vehicle should proceed from one point to another," explains Mats Pålsson, Carmenta's head of sales. His company's system accounts for a vehicle's ability to tilt, as well as its height, weight, turn radius and whether it's tracked, wheeled or amphibious. "Then we can incorporate information about hostile unit locations and use line-of-sight analyses to make sure that the vehicle will stay out of sight."
"Of course, once the vehicle's out there driving, it will have to use its local sensors to avoid anything that's not in the data," adds Moberg, "But it's all there to take that next step."
That's a little modest. The capabilities offered by Carmenta-based command and control systems make them exceptional central engines to facilitate sensor fusion. Over more than 20 years of use, the flexibility and interoperability of the Carmenta Engine has delivered common situational pictures combining sensory, strategic and geolocation information from a vast array of data sources to everything from old ruggedised computers to the latest smartphones.
Terrain analysis for a vehicle equipped with radar, for instance, could indicate where and how obstacles will interfere with its readings as it moves through the environment. As Pålsson highlights, "It's not only a way to analyse the situation you are in at the moment, but it allows you to model a situation that will occur in 20 or 30 seconds in real time. You'll know when you might be in the best position to act towards a target, or just be able to adapt your sensors for an upcoming situation."
Equally, users can calculate routes with different focuses, whether it's speed, fuel consumption, or simply the probability of actually getting to the right destination. The Carmenta Engine can both pick out the best locations for artillery and air defence systems and indicate the best way to get them there. On top of that, the same insights can also be used to realistically model the movements of enemy units through the same environment, accounting for starting points, equipment and vehicle types, cover, sight lines and more.
That's not to mention how terrain and line of sight analysis can improve the operational performance of other types of unit. To take two disparate examples, a Special Forces team deployed in an urban environment could use it to determine where and when a surveillance drone would spot them if they continued along the same route, or a ship that consulted it while navigating an archipelago could work out precisely when a target on the other side of an island would come into view.
Then we come to that missing bridge. The Carmenta Engine fuses geospatial data to form the most complete situational picture possible, overlaying it with dynamic feeds representing, for instance, the tactical scenario, and real-time data layers representing sensor readings - but the environment changes quickly, and the most up-to-date information available can be wrong. Every action you take in that environment, however, changes it, and creating new data and a new context for the next decision. The best support tools must continually build on themselves to respect the developing ground truth.
So, whether it's manned or unmanned, "If the first vehicle that arrives at the bridge discovers that it's not possible to pass there, it sends that information back to the server where the calculation is being made," explains Moberg. "Then future routes will avoid that bridge."
By enabling the shortest possible feedback loop between action and information, the Carmenta Engine creates a virtuous cycle whereby every decision it facilitates works to improve the next one. It's a principle of the company's products. "You have to be as flexible as possible to increase the chance that you can interoperate out in the field," stresses Moberg. "And you need to be able to do things in real time. It's very easy to integrate these analysis functionalities with your existing data sources and data links, and, from the perspective of our technology, there's no limit to how quickly new data can be incorporated - you can instantly feed information back and use it for your next calculation. Our focus is always on reading the data and performing the analysis on-the-fly. That opens up new possibilities."
The potential is built on the fact that, with the right mission critical systems supporting them, separate units with equivalent or complementary objectives can operate more like an adaptive organism. In real time, the experience of each individual element informs the actions taken by all of the others.