Ford: three years of testing self-driving cars in Miami
Marking three years since automotive manufacturer - - began its plans to test and commercialise its , we take a look at what the company has been up to in the last three years to develop the technology.
Argo AI has been working to develop the self-driving technology with a natural experience, while Ford has been building the elements needed for such style of business including its Transportation-as-a-Service software (TaaS), its fleet operations, user experiences, the vehicles and business collaborations.
“In the past three years, Ford and Argo AI have also come to know our fellow Miamians really well. We’ve learned the roads, the driving habits and the incredible opportunities in store for the future of The Magic City,” commented .
Progress made so far…
- Taking pilots from “the page to the roads”
“Running pilot programs has been a priority for us — from testing food delivery with popular brands, to grocery delivery with both major retailers and even local nonprofits like The Education Fund,” said Ford.
Such projects provide Ford with invaluable real world insights to develop four of its core areas: customer experience, business models, operations and its TaaS software.
“Today, we’ve integrated our business pilot programs with Argo AI’s self-driving test vehicles to conduct delivery pilots in autonomous mode. We’re also building our software integration and fleet management capabilities with ride hail and delivery service pilots.”
- Establishing a real estate footprint
To manage a fleet of vehicles, Ford emphasises the importance of space. To support its commercial services after launch, as well as its current testing, Ford has established its real estate footprint for fueling, servicing, cleaning, sanitising, calibrating and optimising the vehicles.
“In early 2018, we established our first terminal in Wynwood [...] We recently established a second facility near Miami International Airport, a large facility we refer to as our Command Center. This space will be the epicenter of our local self-driving business operations housing both our business office and daily fleet operations. That’s not all. This month, we also established a third facility, a terminal in South Beach,” added Ford.
- Expanding vehicle testing
Being one of the most challenging cities to test Argo AI’s self-driving system (SDS), the organisations are exposing the SDS to the unique and challenging situations in Miami. In doing so, Ford expects this to enable the ability to scale to future cities safely and quickly.
“I started with the company as an overnight security guard in the summer of 2018. Now, I’m the Service Technician Coordinator for Ford’s self-driving vehicle fleet operations. When the Terminal first opened in Wynwood, we were operating only a few self-driving test vehicles. Now look at us in 2021, operating a large self-driving test fleet. My journey here has been amazing. To watch the fleet operations grow to the standards where we are now… it’s just awesome!” commented Sherman Bradwell, Service Technician Coordinator, Ford Autonomous Vehicle Terminals.
The future for Ford's self-driving vehicles
In the coming months Ford has established plans to integrate its into its Miami fleet. In addition the two organisations plan to expand their 70 Ford and Argo AI employees in Miami to support testing, operations and business offices, growing its local team over the next few years. Finally Ford strives to continue its collaborative and inclusive approach, working closely with Miami-Dade County, the City of Miami, the City of Miami Beach, the State of Florida and the community at large.
5 Minutes With PwC on AI and Big Data in Manufacturing
Please could you define what artificial intelligence is, and what Big Data is?
AI is the ability of a machine to perceive its environment and perform tasks that normally require human intelligence, and it’s a whole field of different technologies, techniques and applications.
Big data is a set of tools and capabilities for working with, for processing, extremely large sets of data.
How does AI and Big Data work together?
Big data is just one of the enablers of AI, though as we see increasing volumes of data, it’s one of the most important
How can this be applied to a manufacturing setting?
Broadly speaking, there are many benefits of AI, and the use of data, which include reducing costs, minimising human error, and increasing productivity and efficiency. The important thing to consider is any setting - for the use of any technology - is what is the problem you are trying to solve? Be it merely automating repetitive tasks or to reinventing the nature of work in factories by having humans and machines collaborate in order to make better and faster decisions.
Why should manufacturers use AI and Big Data when adopting smart manufacturing capabilities, what is the value for manufacturers?
One view is, again, the economic benefits of AI, which come in manufacturing as a result of:
1. Productivity gains from automating processes and augmenting the work of existing labour forces with various applications of AI technologies.
2. Increased consumer demand due to the increased ability to personalise and tailor manufactured products, along with higher-quality digital and AI-enhanced products and services.
Manufacturing (and construction industries) are by nature capital intensive, and in our 2018 report, “The potential impact of AI in the Middle East,” we estimated that the adoption of AI applications could increase the sectors’ contribution to GDP gains by more than 12.4% by 2030.
How can AI and Big Data help manufacturers to evolve in the Industry 4.0 revolution? What about those already looking at Industry 5.0?
It’s really about the investment you make now, in order to futureproof your business.
We typically see two broad strategies or approaches to the adoption of AI. There are things that we can do immediately, without any recourse to Big Data - which is to adopt technologies we describe as Sensing, those involving computer vision, for example. There are plenty of use cases where these can be used immediately in manufacturing, such as for automatic fault detection. However, there is a longer term play which requires investing in data - getting the right collection mechanisms in place, storage, data governance, Big Data capabilities etc - in order to develop increasingly valuable machine learning driven AI use cases. This is absolutely necessary for long term adoption success.
What is the best strategy for organisations looking to realise the value of AI and Big Data in manufacturing?
AI and Big Data are only one part of a successful smart factory. The organisations that lead on AI adoption are those who have already made the most progress in digitising core business processes. In order get ahead in using AI solutions at scale, there are a number of technology investments and organisational decisions to be made, including:
1. Digitising processes ultimately leads to improved ability to generate data, and in the manufacturing setting - with many 100s of sensors generating 1000s of measurements in real time, the result is Big Data. Data is key to building AI so reliable and accurate data acquisition, management and governance are key. The production line and factories play a critical and direct role in the data-acquisition process.
2. AI strategy, both long and short term, begins with the use cases, the business applications. Manufacturers need to ask where they want to use AI and gather these use cases together and prioritising projects based on a balance of expected impact and complexity of implementation.
Of course, in addition to technology and business processes, people are at the heart of any successful technology adoption. AI teams need to be composed not only of data scientists, also data engineers and solution architects to enable their work, data stewards to ensure accuracy, and increasingly so call “Analytics/AI translators” who are able to communicate with business leaders and technology experts. Culture is also key, and manufacturers need to enable a data and AI-driven culture, building trust in data and algorithms by educating their workforce about AI and its capabilities, how best to extract value. It’s not just the positive of course, but also the risks and limitations, as these when encountered without expectations having been set, can significantly impact willingness to invest.
What are the challenges when it comes to adopting AI and Big Data in manufacturing?
has shown that one of the major challenges to implementing AI is uncertainty around return on investment (ROI). As I said, there is significant investment required for a long term data and AI strategy to be successful, and expectations around the time to see tangible returns must be set realistically.
Many companies also struggle with the data side: collecting and supplying the data that an AI system needs to operate, and ensuring that it is accurate. Again, this speaks to the bigger investments required in digitisation.
Some of the main challenges for manufacturing companies with implementing AI at a scale from our research include:
- 40% → Technologies not mature
- 40% → Workforce lacks skills to implement and manage AI
- 36% → Uncertain of return on investment
- 33% → Data is not mature yet
- 32% → lack of transparency and trust
- 24% → Work councils and labour unions
- 22% → Regulatory hurdles in home & important markets
One element highlighted here, particularly around lack of trust, and labour unions, is that AI is typically misrepresented in the media as “replacing” workers, and taking jobs. Yes, there are efficiency gains to be made from automation, as there have been since the first industrial revolution. But we believe that Data and AI are at their most valuable when they are used to augment workers, enhancing their abilities and the products being manufactured.
Another challenge we’re starting to see emerge is cyberattacks increasingly targeting interconnected equipment and machinery in smart factories. PwC recently hosted a webcast, in cooperation with the National Association of Manufacturers in the US and Microsoft to discuss this.
What are the current trends in AI and Big Data in manufacturing?
- We see companies putting slightly more focus on adding AI solutions to core production processes such as the engineering, and assembly and quality testing
- Safety is of significant importance, with techniques adopted in protocol adherence capabilities (for example maintaining safe distance from specific machinery) being adopted in more every day scenarios for COVID-19 protocol adherence
- There is considerable interest in predictive maintenance for large machinery involved in manufacturing processes, and also supply-chain optimisation
What do you see happening in the AI and Big Data industry in manufacturing in the next 12-18 months?
Honestly, I think we’ll see a continuance of where we’ve already been going for the last 12- 18 months. AI and data are already being used in manufacturing but this use doesn’t get as much attention in the media as, say, healthcare, but the success stories are there, and they will continue as operations continue their digital journeys.