Getting to Level 5 in the Autonomous Driving Game

Welcome to your second engine, an Intel Xeon processor

The SAE (Society of Automotive Engineers) International has a scale that highlights different autonomous driving capabilities. At zero you’re doing all the work but at Level 2, which is where most systems are now, there’s Partial Automation of steering and acceleration/deceleration. The driver still needs to pay attention and intervene, as the focus is on highway scenarios, performing lane centring and cruise control.

Level 3 systems are on the roads too, this is Conditional Automation, where sensors on the car monitor the environment and take full control of the vehicle, with fallback requests on the driver. No dozing at the wheel here, even Level 4 doesn’t accommodate all driving modes, so how do we get to Level 5? The ultimate destination of Full Automation.

Suitably for motorists, there is a roadmap, although, according to Prakash Kartha, Director, Market Development at Intel, the journey will take until 2025 to be realised completely.

“The networks of today are not going to cut it, when it comes to self-driving”

How this takes shape has been simplified by the four pillars of autonomous driving that Intel has identified, namely: in-vehicle compute, data centre, the move to 5G communications and a next generation cockpit to accommodate the new features.

“When we talk automotive within Intel, it is always an end-to-end perspective, wireless is obviously one ingredient but there’s a lot of data centre, a lot of infotainment and a lot of network components to it as well,” says Kartha.

There’s also a lot of data that necessitates this ecosystem with estimates that each self-driving car will produce around 4TB of data a day at Level 5. Kartha has in mind a vehicle with four 8K cameras, four HD cameras, one 360-degree LIDAR and four 180-degree LIDAR, and reckons on a total raw sensor data output of 250Gbps, before a process of sensor fusion.

“Do the maths: it’s pretty clear it starts to add up really fast and that is kind of the basis for our strategy for the whole data problem/opportunity,” he says. “Our expectation is that the networks of today are not going to cut it really, when it comes to self-driving.”

It would certainly seem so, as these components will require a massive bandwidth capability to facilitate data management and the critical requirement of ultra-reliable low latency communication. In this respect, the focus is all on 5G and its increased capacity, that will not only drive the Internet of Things but will get autonomous cars on the move.

But it’s not simply a matter of shovelling sensor data into the cloud and waiting for a response. Kartha describes the in-vehicle compute as, “rather like a second engine within our car” and that it will function as an on-board compute cluster that will handle AI navigation decisions on the fly, path planning and environment monitoring, along with sensor processing and fusion.

“Going forward we believe that you’re going to need data centre scale processors that are obviously power efficient (our folks are on that). Combined with FPGAs (field-programmable gate array), combined with accelerators for things like computer vision,” he says.

Successfully scaling these capabilities is going to be one of the major challenges as we shift gear from Level2/3 systems running at 0.5 to 10 teraflops to a fully loaded Level 5 system operating at 50 to 100 teraflops. But like Kartha says, “our folks are on that” and vehicles equipped with Intel® Xeon® processors coupled with FPGA functions, enabling rapid customisation, is part of the plan.

Meanwhile, back in the cloud, the data centre handles model training that forms the basis of vehicle’s model scoring (or inference), namely, how its machine learning and predictive algorithms respond in the wild. The data centre also performs endpoint management and analytics, but limits need to be observed, as Kartha explains: “There’s an expectation that there’s a certain number of self-driving cars – a fleet, basically – that each data centre can manage. There is a clear correlation between what data centre compute you need, versus the number of vehicles you can manage.”

A distributed architecture of vehicle, network and cloud is what Kartha has in mind to share the burdens of autonomous driving. Rather than conveying data from car to cloud and back, the network itself would provide a vital link in terms of V2X (vehicle-to-everything) cooperative driving, vehicle-to-vehicle communications (e.g. automated overtaking), real-time map downloading and security.

“We really see 5G as the glue that brings it all together,” says Kartha. “Intel is very conscious of the fact that you can’t do this all alone. That’s one of the reasons why we’re part of a bigger consortium of companies that form the 5G Automotive Alliance (5GAA) – BMW*, Audi*, Daimler*, Ericsson*, Huawei*, Nokia*, Qualcomm* and Intel. We see 5GAA as an accelerator for cellular V2X (vehicle to everything) moving forward.” Indeed, such a partnership looks like we’re in for quite a ride come 2025.

*Trademarks are the property of their owners

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