As the battle for the autonomous car market amps up, with Tesla, Waymo and emergent start-ups all vying to be the first to render human drivers irrelevant, the public’s worries about crashes and pedestrian fatalities have slowly abated.
But new research suggests that at least some of the fears about self-driving cars, particularly their potential to exacerbate traffic jams, aren’t unfounded.
Essentially, that ridiculous scene from "The Fate of the Furious" isn’t all that far-fetched.
The latest academic to sound the warning that autonomous driving might worsen traffic rather than improve it is Skanda Vivek, a postdoctoral researcher at the Georgia Institute of Technology. In a new paper whose findings Vivek presented March 4, he argues that not only are internet-connected autonomous vehicles hackable, but hacking even a small percentage of the self-driving cars currently on the road in the U.S.’s largest city could completely stop the flow of traffic and impede the effectiveness of emergency vehicles. Vivek and his team presented their findings at the American Physical Society March Meeting in Boston.
“Compromised vehicles are unlike compromised data,” Vivek wrote in his study’s press release. “Collisions caused by compromised vehicles present physical danger to the vehicle’s occupants, and these disturbances would potentially have broad implications for overall traffic flow.”
Self-Driving Cars Will Still Cause Traffic Jams
After realizing that risk management studies around autonomous vehicles had all focused on the individual crashes caused by, say, poor vehicle reaction time when stopping, Vivek wanted to take a step back and review the situation from a larger perspective. No research had been done to quantify the effect of a “large-scale hack” on traffic flow, and studies focused on the problem of human error tend to find that replacing humans are likely to make roads safer.
To determine the impact of a possible hack, Vivek and his team ultimately turned to percolation theory, a section of probability theory that focuses on the behavior of connected clusters in a random graph, to determine how hacked autonomous cars would affect the already-complicated traffic ecosystem of New York City in real time. The findings? Not great: city-wide gridlock, millions of commuters trapped, and emergency vehicles stuck miles from potential emergencies.