“We’re Building Self-Driving Technology You Can Trust”

Founded: Nov 2016 in Pennsylvania, USA

Category: Artificial Intelligence/Transportation

Primary office: Pennsylvania, USA

Core technical team: Pennslyvania, USA

Status: Private

Employees: Over 1000

Amount raised: $3.6 million  (2 Funding rounds)

OVERVIEW

  • To build new transport solutions by using self-driving vehicles and to make the transportation safe accessible and convenient for all.
  • Software, hardware, maps, and cloud-support infrastructure to power self-driving vehicles
  • To build technology that works in diverse environments (because every city is unique).
  • To address the lack of public transportation options, other affordable alternatives and to respond to the traffic fatalities problems, by using self-driving technology that has the potential to help solve these problems and more
  • Argo team driven by strong values to solve complex problems together, the Argo Way.
  • Argo AI will support local transportation needs by self-driving technology around the world to ultimately make getting around cities safe, easy, and enjoyable for all.

PERFORMANCE METRICS

  • Market cap or value: USD $7.5 billion (July 2020)
  • Revenue: USD $245.2Million (2020)
  • Other metrics: VW and Ford are two main customers.

 

ACHIEVEMENTS

  • Volkswagen invested $2.6 billion in Argo AI: $1 billion in funding and $1.6 billion valuation of its contributed Autonomous Intelligent Driving (AID) company.
  • With the close of Volkswagen’s investment, Argo became a global company and welcomed new teammates to the Argo AI family in Munich. (June 2020)
  • Released Argoverse, a curated collection of high-definition maps and sensor data from a fleet of Argo AI self-driving test vehicles.
  • Launched the first two Argoverse competitions using their motion forecasting and 3D tracking datasets, and invited academic researchers and students to participate.
  • Autonomous Vehicle Technology Award Winner (2020)

Sells

  • Software, hardware (sensors and computers), maps, and cloud support infrastructure that powers self-driving vehicles. Working together with leading automakers, ARGO AI integrates its self-driving system into their vehicles so that they can be manufactured at scale.

Channels

  • Partnerships with top automobile companies Ford and VW
  • Building online communities using social media; @argoai

Competencies

  • Generating Big data, process and analyzing to feed ML/AI
  • Complicated ML/AI system
  • Cloud support infrastructure
  • Argo platform has the largest reported geographic deployment potential of any autonomous driving technology to date, which is significant since scale and geographic reach are important factors in developing a self-driving system that is robust and cost efficient.
  • Global: The first company with commercial deployment plans for both Europe and the U.S.
  • Ecosystem of partners, local community, government

Distinct AI Features

Type

  • As part of their AI revolution, Argo is aiming to rapidly improve the cameras and sensors to generate data with autonomous vehicles. As the amount of information being fed to the IVI (in-vehicle infotainment) units or telematic system grows, vehicles will be able to capture and share not only internal system status and location data but also the changes in its surroundings (all in real time.)ARGO AI’s self-driving system (SDS) is the first with commercial deployment plans for Europe and the U.S. and the automakers’ global reach means the platform has the largest geographic deployment potential of any autonomous driving technology to date.Autonomous vehicles are being fitted with sensors, cameras and communication systems to enable the vehicle to generate massive amount of data which when applied with AI, enables the vehicle to see, hear, think and make decision just like human drivers do.

 

  AI use

  • Using AI to build Autonomous Vehicles that drive themselves like human drivers and mainly to provide these vehicles with the sensory functions and cognitive functions (such as memory, logical thinking, decision making, learning and executive capabilities) that humans use to drive vehicles.

Rate of return on customer’s investment to make AI work

Immediate:

  • Volkswagen and Ford earned reputation within the autonomous industry and shared the development costs.

Long term:

  • Volkswagen joined Ford in investing in the self-driving tech company to better serve their future customers while improving cost and capital efficiencies.

Databases

  • Cloud support infrastructure

Quantum Computing

  • While self-driving cars will have OTA (Over-The-Air) electronic communications capabilities (acquiring system updates for the on-board AI system from the cloud and to push data collected by the self-driving car up to the cloud), quantum computers could improve on cloud efficiencies.
  • Another facet of utilizing quantum computers while in-the-cloud, would be to use them for scheduling car traffic and undertaking traffic management. Once there is a prevalence of driverless cars, they could interact and coordinate to reduce traffic congestion. Besides electronically communicating with each other via V2V (vehicle-to-vehicle) electronic communication, and via V2I (vehicle-to-infrastructure) communication, they might also interact with a “master” traffic management system that is trying to even out the flow of thousands of cars on the roadways. That being said, the on-board AI could certainly consult with the quantum computer in the cloud, doing so to get an added “opinion” about a driving issues.

Resources

Assets

  • Fully Autonomous cars technology
  • Navigation technology
  • Computer science skills
  • Diverse team of high skilled individuals

Processes

  • The ability to move goods around in a highly coordinated fashion
  • Autonomous ride-sharing services
  • Designated drop-off and pick-up zones in line with cities improvement
  • Connected and optimized transportation systems
  • Rigorous development in the lab, via computer simulation
  • The testing process: 1. Development testing, 2. Simulation/Resimulation testing, 3. Closed course testing, 4. Public road testing

Priorities

  • Safety is the foundational value upon which all others are built
  • Customers trust and acceptance
  • Data and existing controls evaluation to identify and implement areas of improvement
  • Risk management and comprehensive risk assessments
  • Promote the culture through training and communications