Eliminating Traffic Jams with Self-Driving Cars

Featured Faculty: Alexandre M. Bayen

 

Professor Alex Bayen, Director of UC Berkeley's Institute of Transportation Studies, is featured in Fortune On Demand and PBS Terra on the use of artificial intelligence (AI) to eliminate traffic jams. 

Phantom traffic jams, or stop-and-go traffic, are inefficient and dangerous. Fortunately, self-driving vehicles can reduce and improve traffic jams by guiding human-controlled vehicles on the road. For traffic to flow smoothly, drivers must maintain a consistent speed and distance from other vehicles. However, humans tend to speed up to meet the car in front of them, causing phantom traffic jams. Bayen believes that automated and human-controlled vehicles could work collectively to smooth traffic flow. 

In a recent study, when an automated car led human-controlled vehicles, stop-and-go traffic was eliminated and gas usage was reduced by 42%. Bayen's team has also simulated traffic using models on loops, ramps and figure-eight courses, with automated cars improving flow among human-controlled vehicles.

How close are we to seeing the positive impact of self-driving technology on our roadways? Most companies have installed technology in new cars that could allow for the rapid implementation of AI, allowing automated cars to communicate with other AI vehicles and even the roadway. However, a main challenge is in establishing communication between vehicles, and coordinating efforts among legislators, researchers, and companies.

Published 03/15/2021