Alexandre Bayen is a driving force behind mixed-autonomy traffic
Coordinated automation could improve traffic flow, boost efficiency, and slash emissions. A combination of machine learning, big data, and Amazon Web Services is making this future possible.
By Sean O'Neill | August 23, 2021
The smooth-flowing traffic of the future is just around the corner. Advances in vehicle automation are converging with developments in machine learning (ML) and cloud computing to create self-driving vehicles that not only control themselves safely, but also have an oversized beneficial effect on the journeys of all the regular drivers on the road around them. Welcome to “mixed autonomy traffic”.
Leading the pack into this future is Alexandre Bayen, the Liao-Cho Professor of Engineering at the University of California Berkeley and director of its Institute of Transportation Studies. An expert in control and optimization, Bayen is playing leading roles in multiple transportation projects, ranging from cutting-edge, open-source traffic simulation and optimization, to large scale freeway observation that involves putting automated vehicles into real traffic to explore the impact of ML-derived self-driving behaviors. These automated vehicles also have human supervisors at the wheel, ready to take over the vehicle at any time if needed.
Before delving into Bayen’s work, an example of the promise of mixed autonomy traffic is in order.
This video is from a 2008 experiment in which people are attempting to maintain the same speed while driving single-file around a circular track.
Alexandre Bayen is the Liao-Cho Professor of Engineering at the University of California Berkeley and director of its Institute of Transportation Studies. Bayen plays leading roles in multiple transportation projects.