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Berkeley duo's plan to solve traffic jams: hyper-fast lanes for self-driving cars

These days there are so many self-driving cars coming down the pipeline it seems inevitable they’ll soon be stuck in a robot traffic jam – just like the human-piloted cars of today. Well, not if Anthony Barrs and Baiyu Chen get their way.

The two graduate students at the University of California, Berkeley, have devised a system that would have tightly-packed clusters of autonomous vehicles zipping past local traffic at speeds of more than 100mph, all on existing roadways. They call it Hyperlane, and it works a lot like high-speed toll lanes already do, only with a central computer controlling everything.

Although fully autonomous cars are not yet legal on most public roads, manufacturers like Volvo and Tesla already offer autonomous features on their vehicles – adaptive cruise control and, in some cases, systems that steer the car with limited driver input.

Barrs and Chen came up with Hyperlane after taking a close look at proposed high-speed rail systems like the troubled Los Angeles to San Francisco route.

The bottom line is that high-speed rail is expensive – at its current, ever-rising cost estimate, the California rail project would cost $139m per mile. So the two researchers concocted a mashup of bullet train and dedicated toll lane, which they say would only cost about $12m per mile.

“Long story short: high-speed rail didn’t pencil out over here,” Barrs said of the perennially back-burnered California rail project. “We’re primarily looking at cities with major nodes that need to be connected, where high-speed would create high value.”

Barrs and Chen said Hyperlane would work well in the San Francisco Bay area, as well as in metropolitan regions like Dallas-Ft Worth and Baltimore-Washington. It wouldn’t work in high-density metro areas with no space to expand roads – New York City, for example – or in medium- and low-density cities where lower traffic volume wouldn’t justify the cost of construction.

“If you’re in DC and you have this Hyperlane connecting the DC metro area, you can now fly out of the Baltimore-Washington international airport much more easily,” Barrs said of Hyperlane’s theoretical capability, adding that battling through road traffic and spotty public transit options to reach BWI from the southern end of the Washington DC area can take a long time. “This creates additional transportation options.”

Hyperlane works a lot like existing dedicated commuter lanes, only instead of paying extra to use higher-speed, lower-congestion lanes in a human-driven vehicle, the separate lanes are only for autonomous vehicles. After entering an acceleration lane, Hyperlane’s central computer takes over the car’s functions and finds a slot for it in the already fast-moving traffic in the dedicated lanes.

Barrs and Chen said vehicles would travel at speeds up to 120mph, and that the centralized computer control – which would be in constant communication with each vehicle using emerging 5G technology – would allow for a more tightly-packed traffic pattern.

“We liken the Hyperlane network to an air traffic control system,” Barrs said.

Sensors in the road would evaluate traffic density, weather hazards, accidents and other changes, prompting the system to adjust vehicle speed as necessary.

Like Uber’s pricing structure, fees for Hyperlane would be based upon demand.

Construction could be approached in two ways, depending upon the space and funding available: carving lanes out of existing highways, or building new lanes next to existing highways. Chen said that using vehicles sized appropriately for different purposes – microbuses for office commuters and small pods for pizza and package delivery, for example – would make the best use of space on the Hyperlane system.

“Berkeley to Palo Alto would be a 40-minute trip, and by splitting among five or six people, would cut down the cost,” Chen said. That 40-mile trip can take more than two hours during rush hour.

Barrs and Chen recently won top honors for Hyperlane in the Association of Equipment Manufacturers’ Infrastructure Vision 2050 Challenge, but their idea is still in its theoretical stages. They said they still need to find investors to back physical testing of the system, as well as theoretical testing beyond the Bay Area.

“There’ s a step that exists between theoretical and physical testing, and that’s getting a coalition of partners to fund it,” Barrs said. “We’re looking at state transportation officials and transportation companies like Uber and Lyft.”

In the meantime, robot cars can get in line with the rest of us.

#transportation #innovation #roads #autonomousvehicles #safety