Recently I have been diving deep into researching the possible futures of autonomous fleet movement dynamics. When the time comes, how are AVs going to flock? This research had led me to uncover many of the necessary advancements in machine perception, mesh networking, and deep learning that will provide for the ground-breaking AV opportunities we have been reading about in the headlines. Yet, I have also been turning over stones of possible modeling traditions that might help us prepare for the AV future.
As a movement nerd, what excites me most about self-driving cars is the possibility that their spatial navigation will be safer and more energy efficient than what has historically been achieved by humans. How might this look? A great example comes from the field of evacuation dynamics. Scientists who model crowd dynamics in emergency settings go to great lengths to factor in all the psychosocially-driven, yet spatially-inefficient, ways people move their bodies when they’re freaked out. For visuals, watchwhat kind of modeling is required to deal with “inter-agent avoidance”, and check out this great talk by Dirk Helbing.
I know there are people out there who will forever want to put their hands on a steering wheel, but when I think about the population at large, it’s hard for me not to get excited about iterating on an existing system that is so riddled with human error. How will our autonomous fleets move when their navigation is unfettered by daydreaming, inebriation, and texting?
I have been seeking out companies who are beginning to unpack this question, and was excited to read Lyft’s recent Medium series discussing their approach to solving data science problems. I believe their market simulator presents a great opportunity for developing optimal models of autonomous fleet navigation, although I am sure charting this horizon is just one of many interests for them.
Perhaps we can get a head start on the questions.
- How will we model autonomous vehicle dynamics? Specifically, how quickly will we scale the shift to machine-optimized motion planning in our models of autonomous fleet simulations?
- How will autonomous vehicle dynamics affect urban design, which in turn will impact autonomous vehicle dynamics? How will we include such evolutions in our models?
- How will increased navigation efficiency impact revenue models that rely on dynamic pricing?
- How will we measure the degree to which passengers trust the safety of our fleet as it becomes more and more autonomous?
Those that anticipate these changing dynamics will be better equipped to leverage their shiny new toys when they finally arrive. Which, with many in the industry elbowing towards 2021, may be sooner than some anticipated (and, of course, later than others).