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(TORONTO, ON – January 13th, 2017) I started working at NASA Ames Research Center in Silicon Valley in 1998. After 12 years there, I went to Xerox PARC, where I served as director and ran research on multi-agent systems and human-machine interaction. It was at NASA, though, that I created much of what I’m putting to work for Nissan today. We started with development of a simulation language that allowed us to model human behavior and multiple people working together. We were looking at how people might live on Mars and work with people back on Earth, as well as autonomous systems, including robots and smart habitats on Mars. We started first simulating this with our language, but once we started running this language in real time it also became a programming language for autonomous systems in general.

We started building intelligent agents for robots, for Mission Control and for the habitat. Then we added a speech-dialogue system to this – now we could have astronauts talking to their autonomous system, including the robot and the habitat, as well as to systems in Mission Control. We put it to work in space; we actually built an intelligent agent in the space suit to monitor the astronaut’s health autonomously.

We helped design how robots would work on Mars with people on Earth. After this success, we were asked to automate flight controllers in NASA’s Mission Control Center for the International Space Station. The final project I did at NASA used my computer language to automate a flight controller for the ISS. This system went live in 2008 and it’s still in use, with all communication from the ISS taking place through it.

To build the autonomous system for a vehicle on Earth is really like building a robot that drives 80 miles an hour very close to other robots. That is very different from Mars, where there are not that many people – at least not yet! Many issues come up when you think about humans interacting with each other and with robots, because the car needs to be on the road with other people – pedestrians, bicyclists and other cars. The idea of multi-agent modeling becomes key: knowing what everybody is doing, so that the car knows not only what it needs to do itself, but also its relationship with others on the road. In urban areas, we have to deal with pedestrians, bicyclists, motorcyclists, cars, animals – the whole spectrum of interaction becomes a very important study. The work that I did at NASA is very relevant in this context.

In connection with these driving environment issues, I started a research project in North Holland, a Dutch province with a world-leading traffic management system. Every traffic light there is connected with all the rest, and they communicate dynamically based on how busy the roads are to decide when they will change. We used data from these traffic lights in intersections and built machine-learning algorithms to predict when a traffic light will go red or green, based on how far one is from the light.

We now use this algorithm with our autonomous vehicle software, so as the vehicle drives it gets information from the traffic system to predict how long the light will be red or green. Ideally, the autonomous vehicle doesn’t have to stop; it can automatically reroute and take lights that are green. As we optimize the autonomous system to avoid stopping for lights, we also send information from the vehicle back to the system so as to optimize traffic management on a larger scale.

In autonomous vehicles now, most of the technology is based on software and on artificial intelligence (AI). There’s no better place to do this work than in Silicon Valley. The NASA Ames Research Center was one of the first places where robots and autonomous systems were put together, and all the technology around autonomous vehicles was developed at universities and companies in the area. In the early 2000s, many AI researchers from around the world came to Silicon Valley to join the IT industry. Nissan realized this in the mid-2000s: If we wanted to be serious about building autonomous vehicle technology in house, we had to have a presence in Silicon Valley.

The Nissan Research Center has relationships with people from Stanford and UC Berkeley – which provide a key talent pool for us, too – and we’ve set up very close, fruitful research collaboration with NASA, just down the road from us. We’re speaking with a number of Silicon Valley firms to see how we can work together. So it’s vital to be in this region.

Looking Toward the Future of Mobility

At NASA, I researched how humans and robots would work together on Mars in the future. When Nissan asked me to do that for vehicles on Earth, it was very difficult to say no. I have a particular view on how humans and autonomous systems should work together, and I really appreciated that people at Nissan had a similar idea. Nissan believes that mobility is for the good of society; that’s one of the reasons I decided to come here.
It’s exciting to think about a society where the right mobility system is used for the right purpose at the right time. We’re going to have trains interacting with shared vehicles that can seamlessly take me from work to home to school to pick up my children. Whatever I need to do in my life will be seamlessly integrated with mobility services at different places. I don’t believe that public transportation should go away. By integrating our vehicles – our trains, our planes, even our bicycles together into a society where we have more space for parks and beautiful landscapes – we’ll have more space for people.

Nissan’s vision of virtually zero fatalities and zero emissions is really a great motivator for doing research to move in that direction. Autonomous technology can be applied not only inside the vehicle, but also in the cloud, in trains and in other transportation systems. We should always be efficient in the way we move around and interact with others.