The University of Waterloo’s “Autonomoose” is a self-driving Lincoln sedan developed by, and undergoing real-world testing via, a team of graduates and engineers at the University of Waterloo.
What’s remarkable is the car’s capabilities are on par with the other fully automated rides researched and developed at the manufacturer level.
Remember, this ‘Moose is the product of a team building on a university-type budget, and not some deep-pocketed manufacturer laboratory.
To put the cost of developing the fully autonomous car into perspective: the automotive and related industries has spent $8 billion on the tech in the past five years. It’s not the hardware, but rather the software that’s the complex part of the equation.
The data-cruncher has to be able to react to any given situation correctly, and it must do so each and every time.
While the Lincoln MKZ Hybrid-based ‘Moose is a somewhat ungainly-looking thing, it functions to a tee, and it did so on a very wet and rainy day. In many instances, inclement weather would be enough to postpone the demonstration. Not here, as Moose went out and completed its automated driving tasks without missing a beat.
Sitting atop the roof are the cameras and the lidar (light detection and ranging) unit. There are eight cameras, which give a 360-degree view around the vehicle. The information generated by the “eyes” is key to autonomous driving.
The problem is most of the software that looks at the camera-generated 2D images will discard the information if the image is less than picture-perfect. In ‘Moose’s case, as long as 70 to 75 per cent of the detail in the image is usable, the system continues to crunch the data. In this case, the raindrops on the camera’s lens were not enough to render the image’s information useless.
The lidar unit stands proud in the centre of ‘Moose’s roof. It scans the environment 10 times a second to create a second three-dimensional view of the surroundings. It not only detects trees, guardrails and other obstacles, it’s smart enough to learn its surroundings, so the next time the computer “sees” a familiar building it has an important point of reference.
The battery of cameras and the lidar work in unison and develop a complex “map” that identifies everything in the immediate area and what lies further out.
Another key part of the puzzle is the hi-definition mapping. It’s designed so the system does not need painted lines to know where a lane lies or where the next stop sign sits—it is all contained in the data. When viewed on a screen, it shows three basic lines. There’s one for each side of the lane and a third that traces the middle of the lane, which is where the car needs to be.
Three-dimensional dynamic object detection tracks other vehicles in real time. Once it latches onto a car, it tracks its progress until the car has passed the ‘Moose. It also predicts the probable path of the car to ensure it is not moving into ‘Moose’s lane.
All of the information is fed to a deep neural network to determine the best course of action at any given time. It is a powerful data-cruncher that takes a snapshot of the information 10 times a second, and then makes decisions based on the input.
Of course, there are myriad other sensors, including accelerometers and wheel-speed sensors, and it has a rule-based behaviour-planner. The latter recognizes stop signs—the convention mandates ‘Moose wait for three seconds at a stop sign before taking one final look around and making the decision to proceed through the intersection.
This sort of wait time is long by normal driving standards, but it is necessary—is the pedestrian typing on their cellphone simply standing on the corner or about to step out into the intersection without looking?
Now the mechanicals take over to do the acceleration, steering and, if necessary, the braking.
The demonstration saw ‘Moose negotiate an intersection, avoid a bunch of hay bales with a pumpkin atop, and avoid a parked car. The ability to avoid the hay bales came down to the fact that at any given time ‘Moose’s system is constantly calculating a number of possible driving paths.
When the hay bales blocked the path it was following, ‘Moose smoothly picked an alternate path, pulled out to avoid the bales, and then got back into lane smartly. The intersection test was equally flawless in spite of the wait time.
When it recognized a stationary car in its lane, ‘Moose came to a halt behind it. Here, the reaction mirrored just about all other autonomous cars—it will sit and wait for the car ahead to move, as it does not yet have the capability to recognize the fact it is parked or has broken down.
When vehicle-to-vehicle communications comes to pass, the parked/broken down vehicle will let the rest of the world know so other autonomous cars will be able to negotiate a safe way around it. Until then it requires driver intervention. When this same scenario was played out in a Level 3 production Audi A8, it produced the same result.
The Autonomoose is a remarkably advanced autonomous car that does not take a back seat to any of its peers. The fact it is being developed and tested here is a very reassuring sign—Canada can compete with the best minds in the world.
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