smart cities

This tech startup envisions Houston as a self-driving city

Aatonomy sees autonomous vehicles as inefficient and unsafe. That's why the Houston startup is doing something differently. Sean Pavone/Getty Images

When there isn't a global pandemic, nearly 7 million people drive around Houston, and an estimated 77,000 people commute for more than an hour and a half to work. Drivers spend $1,376 and waste 31 gallons of fuel a year — to sit in traffic for what adds up to 75 hours each year.

When Wilson Pulling moved to the city two summers ago, he set out to fix all that traffic-sitting using autonomously driven cars, but not the high-priced ones that Uber and Tesla have designed. These are your regular, three- or four-year-old Honda Civics and Kia Sorentos — the cars you already own.

In 2016, Pulling founded had Aatonomy with his partner, Yang Hu, based on their thesis work from Cornell's Computer Science program. Moving the company south after two years operating out of San Francisco, they aimed not to build the self-driving car of the future, but to make the cars that Houstonians are wading through congested freeways in today drive themselves.

"Everyone doesn't get to buy a Tesla. They're driving their Corollas," Pulling says. "The way autonomy is going right now, that person is never going to benefit. We are the only way."

The company's technology attaches a wireless receiver to the car, which has to be from at least 2016 to work with them. Then, Aatonomy places sensors all along the roads and streetlights. The sensors and receiver communicate with each other, and enable autonomous driving.

Imagine, Pulling says, a 30-mile of I-45 with Aatonomy's sensors. You'd roar up the freeway, handling the controls. Then, the car's computer, under guidance from Aatonomy's network of sensors, would take over. You'd sit back, the car will navigate the traffic along with the other cars — and if all the cars are autonomous, Pulling says, the algorithm could slash congestion. When your car exits the freeway, you'd take back control.

That stretch of freeway would cost $26 million for 200,000 commuters across Houston, Pulling says, but other self-driving cars cost around $250,000 per vehicle — summing up to $50 billion for those same commuters. And Pulling says the Aatonomy system is a safer bet than the way Uber's autonomous driving. Uber's car once killed a pedestrian because, somehow, the company didn't program it to avoid people jaywalking. But because Aatonomy will manage sensors all over the street, the company will be able to monitor potential accidents more quickly than an Uber car would.

"This is a really radically different approach to a technology that, frankly, a lot of people have lost a lot of faith in," Pulling says.

Aatonomy's approach requires a smart city commitment — but the city of Houston is already buying in. First, Aatonomy, a member of the Ion Smart and Resilient Cities accelerator's inaugural cohort, got a short-term project with Aatonomy and Verizon to mount intersection cameras for studying how to prevent collisions with pedestrians on the Northside.

Additionally, the city has also greenlit a two-year pilot with Aatonomy to automate a bus route in downtown Houston. The aim, Pulling says, is making a "proof-of-concept" before rolling out sensors across I-45 — but it's also to use Houston as proof that autonomous driving can be achieved, but from a different angle than Uber.

"Self driving cars don't work. That's our thesis," Pulling says. "That's why we're building self-driving cities."

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Building Houston

 
 

This UH engineer is hoping to make his mark on cancer detection. Photo via UH.edu

Early stage cancer is hard to detect, mostly because traditional diagnostic imaging cannot detect tumors smaller than a certain size. One Houston innovator is looking to change that.

Wei-Chuan Shih, professor of electrical and computer engineering at the University of Houston's Cullen College of Engineering, recently published his findings in IEEE Sensors journal. According to a news release from UH, the cells around cancer tumors are small — ~30-150nm in diameter — and complex, and the precise detection of these exosome-carried biomarkers with molecular specificity has been elusive, until now.

"This work demonstrates, for the first time, that the strong synergy of arrayed radiative coupling and substrate undercut can enable high-performance biosensing in the visible light spectrum where high-quality, low-cost silicon detectors are readily available for point-of-care application," says Shih in the release. "The result is a remarkable sensitivity improvement, with a refractive index sensitivity increase from 207 nm/RIU to 578 nm/RIU."

Wei-Chuan Shih is a professor of electrical and computer engineering at the University of Houston's Cullen College of Engineering. Photo via UH.edu

What Shih has done is essentially restored the electric field around nanodisks, providing accessibility to an otherwise buried enhanced electric field. Nanodisks are antibody-functionalized artificial nanostructures which help capture exosomes with molecular specificity.

"We report radiatively coupled arrayed gold nanodisks on invisible substrate (AGNIS) as a label-free (no need for fluorescent labels), cost-effective, and high-performance platform for molecularly specific exosome biosensing. The AGNIS substrate has been fabricated by wafer-scale nanosphere lithography without the need for costly lithography," says Shih in the release.

This process speeds up screening of the surface proteins of exosomes for diagnostics and biomarker discovery. Current exosome profiling — which relies primarily on DNA sequencing technology, fluorescent techniques such as flow cytometry, or enzyme-linked immunosorbent assay (ELISA) — is labor-intensive and costly. Shih's goal is to amplify the signal by developing the label-free technique, lowering the cost and making diagnosis easier and equitable.

"By decorating the gold nanodisks surface with different antibodies (e.g., CD9, CD63, and CD81), label-free exosome profiling has shown increased expression of all three surface proteins in cancer-derived exosomes," said Shih. "The sensitivity for detecting exosomes is within 112-600 (exosomes/μL), which would be sufficient in many clinical applications."

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