Turning cars into robot traffic managers

As car companies increasingly tout semi- and fully-autonomous features -- including lane control and "autopilot" -- and 29 states have enacted legislation related to self-driving vehicles, UC Berkeley transportation researchers are addressing this emerging era of smart vehicles with a tool that uses machine learning to manage traffic where autonomous, semi-autonomous and manned vehicles share the road. The project, called Flow, rolled out its first proposed standards for solving real-world traffic problems, including easing bottlenecking on the San Francisco-Oakland Bay Bridge, today (Monday, Oct. 29) at the Conference on Robotic Learning in Zurich, Switzerland. Many traffic researchers are addressing smart-vehicle integration, but compared to models that use manually derived algorithms to design controls like metering-light timing, machine-learning-based controls can provide benefits like lower energy consumption and novel traffic-management solutions that are out of reach of human calculations. "Flow solves large-scale, multi-vehicle problems by using simulations that are much more efficient than what can be produced without the aid of artificial intelligence," said electrical engineering and computer sciences professor Alexandre Bayen, director of the UC Berkeley Institute of Transportation Studies and the study's principal investigator. "And we've made it a cloud-based, open-source system so the development community can continue to build on it."