It’s an amazing time for those of us in the fields of robotics and artificial intelligence. We’re starting to see consumer products and services powered by the capabilities that we and our predecessors spent decades developing and testing in labs across the country. And one of the flagship opportunities enabled by these capabilities — autonomous vehicles — is finally on the horizon with initial commercialization plans in place.
Yet, we need to continue stretching the frontier to truly unlock the potential of autonomous technology by developing the most advanced capabilities that will enable large scale, global deployment. That’s why we’re forming the Carnegie Mellon University Argo AI Center for Autonomous Vehicle Research. To fund the Center, Argo has pledged $15 million over five years to fund a team of five world-renowned faculty leaders and support graduate students conducting research in pursuit of their doctorates, to push the envelope on the next-generation of self-driving technology.
In an era where more faculty than ever are leaving the research arena for industry roles, it’s crucial to support academia while fostering the next generation of leaders.
While the team at Argo AI sees a pathway to achieve initial commercialization opportunities for self-driving technology, there are still advancements required to be able to perceive and navigate autonomously in the most complex, open conditions with dramatically lower compute power. And until we’re able to do so at scale, the visionary benefits that have been spelled out for society won’t be achieved.
For instance, how can autonomous vehicles “see” their surroundings and operate safely in adverse weather such as very heavy rain, falling snow and fog? How can we reduce or eliminate reliance on high-definition maps without sacrificing safety and performance? How can autonomous vehicles reason in highly unstructured broken-traffic conditions commonly found in some big international cities, where actors on the road completely ignore any road rules? How can we reduce our need for labor intensive high definition map data when moving to new cities? Once fleets of vehicles are deployed, how do we efficiently leverage the experiences of an autonomous fleet to ultimately obtain exponential improvements beyond the original launch capabilities?
What is unique about this center is that it will look at the task of autonomous vehicles from end-to-end, including perception, decision-making and actuation, yet push the envelope to advance beyond our first-generation system enabling us to serve more cities, operate more efficiently and interact safely in the most complex environments. This is the very essence of the role academic research plays in advancing science.
To build robotic systems that can operate in the real world, a massive amount of infrastructure is needed for continuous software integration, large-scale sensor data collection and processing, and fleet maintenance and refinement. Simply put, neither academic institutions nor industry can do it alone.
Another benefit of being on campus is that it will spur engagements on other topics that are important to us, including safety policy and ethics, so we look forward to working across disciplines to ensure we’re taking a holistic view.
This Center builds on Argo’s already existing collaboration with CMU and Georgia Tech, while introducing a unique model for academic engagement with unprecedented access and openness. In addition to myself and Simon Lucey serving as the Center’s faculty leaders, it’s a privilege to also have John Dolan, David Held and Jeff Schneider as part of the team. John’s focus is on mechatronics, systems engineering and safety; David’s is machine learning; and Jeff’s is machine learning, computer vision and perception.
Argo will provide access to data, infrastructure, and platforms to CMU students engaged in autonomous vehicle research. Notably, this access is combined with a commitment to produce open research, software, and datasets that can be used by the research community at large. This reflects Argo’s broader commitment to support the next generation of computer vision and machine learning experts alongside some of today’s brightest academic leaders.
The Carnegie Mellon University Argo AI Center for Autonomous Vehicle Research further builds on our growing connection to the academic community. Recently, Argo AI launched Argoverse™, a collection of sensor data and HD maps for computer vision and machine learning research to advance self-driving technology. The researchers and faculty working in this center will not only have access to Argoverse, they will have access to far more data and knowledge through their direct collaboration with Argo.
We view this Center as an important step in advancing research addressing the challenges that will enable commercialization of self-driving technology on societal scale and we look forward to the improvements this Center can help bring to the field of self-driving vehicles.