“Trucking is the tip of the spear,” a spokesperson for autonomous-truck developer Aurora told Automotive News. The company’s Aurora Driver system promises to offer efficiencies similar to other AV developers’ products, with increased operational hours, fuel and maintenance efficiencies and improved safety. The Driver, however, is designed for wider application.
“Trucking enables us to rapidly and efficiently move into adjacent verticals, like ride-hailing,” the Aurora official said. “Trucking is well over a $700 billion business in the U.S. By going first to market with an autonomous truck, we can build a strong, scalable product and revenue base. That experience and scale will be inherited by our ride-hailing product.”
Aurora has a pair of manufacturing partners, Paccar and Volvo, which combined account for more than 40% of all Class 8 trucks sold in the United States. That makes a path to revenue from implementing Aurora Driver clear.
Unlike other AV developers, Aurora is leveraging what it learns from trucking and applying it to other use cases and vehicle types—including ride-hailing—by developing a “common core” of software, hardware, infrastructure and development tools.
Partnering with manufacturers allows AV developers to leverage the manufacturing and hardware experience of those manufacturers, and it puts the existing relationships of those manufacturers at Aurora’s fingertips.
“OEMs have relationships with large shippers and carriers because they’re serious players that need to operate with such efficiency and speed, so the value of trust between the two is high,” the company says.
“We recognized early on that if we didn’t have the endorsement of a manufacturer, that would impact our ability to launch commercial pilots with big networks this year and ultimately launch a self-driving truck without a safety driver by late 2023.”
This is not the only symbiotic relationship AV developers have, however.
Handling the computing scale of Aurora’s needs requires computing power that can scale and adapt to geographically dispersed teams handling huge amounts of data. “In June 2021, we exceeded 5 billion virtual miles as our enhanced virtual development tooling and an expanded team allowed our engineers to chew through an average of over 22 million miles each day in our virtual testing suite,” the company says.
To handle that data, many AV developers look to AWS.
“During development, AV companies have geographically distributed test fleets collecting tens of terabytes per vehicle every day,” says Vijitha Chekuri, global business development leader for autonomous vehicles at AWS. “That data quickly scales up to petabytes for companies with fleets with dozens or even hundreds of test vehicles.”
“The processing and usage of the vehicle data requires pipelines that are agile enough to enable unpredictable iterations upon driving models and algorithms for faster time to market,” says James Barr, an AV business development specialist at AWS. “Autonomous-vehicle development teams also require intelligent data management, training, simulation, verification and validation. Implementing these functions requires cost-effective mechanisms to leverage thousands of standard and specialized compute instances to develop and deploy self-driving functionality.
“If you buy hardware and use on-premises storage and computing, you’ve paid for it whether or not you’re using it for a highly unpredictable set of workloads. One of the key value propositions of the cloud is spinning up the latest in computing power when you need it and switching it off and not paying for it when you don’t. This concept is key when you consider the iterative nature of AV development.”
The flexibility, security, privacy and complexity of the data needs, both in terms of scale and management, also discourage home-grown hardware, says Chekuri. AWS offers online and offline data transport solutions that can quickly collect data from AV vehicles and upload it to “data lakes” in the cloud, where it can be stored and managed long term. The use of autonomous data lakes on AWS provides AV customers the ability to cost-effectively store the data, search, analyze, visualize and be leveraged by diverse development teams for downstream workloads such as simulation and training.
“Storage is one of the biggest cost drivers for AV development,” Chekuri says. AWS offers six classes of storage for cost-effective archiving and accessibility. AWS Intelligent tiering features allows data to be moved automatically between classes based on usage, allowing information to be quickly accessed at one tier of pricing or be archived long term at a much lower cost.
“It may be that regulators require 10 to 15 years of data retention,” Chekuri says, “so low-cost, archival solutions like Amazon S3 Glacier and S3 Glacier Deep Archive are essential for AV companies.
“Once data is uploaded, products like AWS Glue can be used to crawl, discover and catalog data for analysis.” That helps identify unusual scenarios that may be hard to diagnose, such as a repeated sensor failure in a specific locale or repetitious errors in a certain driving situation.
According to estimates from Synergy Research, AWS accounted for 32% of the cloud infrastructure market in the first quarter of 2021. That scale means it can offer solutions that other providers cannot, something that many AV developers are aware of.
“AV companies Toyota, Mobileye, Aurora, Torc Robotics and Lyft are all building autonomous-driving models and HD maps on top of AWS,” Barr says. The company’s long history of assisting such developers with a constellation of needs makes it a highly capable partner.
“We have reference architectures, open source code, partners and best practices for the various phases of AV development workflow ,” Barr says.
Because of the frequency with which developers have asked AWS for niche solutions, it also makes a variety of highly specialized tools available. AWS SageMaker Ground Truth provides 2D and 3D first-party managed labeling services. SageMaker, for instance, allows developers and data scientists to quickly build, train and deploy machine-learning models at scale, simplifying workflow and allowing faster iteration.
“Our customers need hyper-scale infrastructure that is secure, global and compliant with their specific needs,” says Jon Jones, director for AWS Compute and AI/ML services, including autonomous vehicles.
“We’re helping the leaders in long-haul autonomous-trucking technology with their infrastructure, AI and machine-learning needs just as they are helping their manufacturer partners build the trucks of the future.”