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Awad Abdelhalim: The not-so distant future of self-driving cars

The following story was written in April 2019 by Phillip Andersen in ​ENGL ​4824​: Science Writing ​as part of a collaboration between the English department and the Center for Communicating Science.

This photo shows a young man with brown skin and dark hair wearing a blue gray suit with a dark tie that has a conference name tag clipped to it. He is smiling  and is standing in front of a skirted conference-type table with research posters on the wall behind him.
Awad Abdelhalim presenting his research on transit signals. (Photo courtesy of Awad Abdelhalim)

Self-driving cars, the newest innovation in transportation, seem to incite as much excitement as concern. The idea is that a combination of GPS tracking and a variety of sensors including cameras, radars, and microphones—alongside the existing automotive hardware—can enable a car to autonomously navigate from point A to point B just as reliably as, if not better than, a human driver.

    While the technology has come a long way since its humble beginnings over a decade ago, there are still many legal, infrastructural, and social considerations to be dealt with. One of the people researching these issues is Awad Abdelhalim.

    Abdelhalim decided that transportation systems engineering would be his future when he realized he could combine two things he loved: engineering and travel. He began as an undergraduate in civil engineering in his native country of Sudan, and now, after completing his master’s degree in transportation systems and infrastructure at Virginia Tech, he is remaining to pursue his Ph.D. degree.

    Abdelhalim gained a deep and data-intensive understanding of traffic systems during his master’s degree work. His research at the Virginia Tech Signal Control & Operation Research and Education lab (VT-SCORES) with Dr. Monty Abbas is in the area of intelligent transportation systems, where he has carried out research focused on big data analytics, traffic modeling, and a technology called “vehicle to vehicle and infrastructure communications.” 

    As the name implies, this is a system that would allow vehicles and traffic lights to communicate vital information to each other. For his master’s thesis, he studied an application of the technology through developing a simulation model for Blacksburg Transit vehicles. In this model, a wireless communications system would enable buses  to coordinate their departures and arrivals with the timing of traffic lights, allowing them to spend as little time as possible at red lights and thus to transport more people more quickly. The idea is that buses carry the most people, and since the goal of an efficient traffic system is to get people where they want to go as quickly as possible, it makes sense to prioritize buses.

    Abdelhalim believes this sort of optimized approach to traffic can eventually be implemented on a grand scale. The model he developed using Blacksburg traffic data could reduce bus delays at major intersections by up to 60 percent while increasing car delay at intersecting streets by only  5 percent.

    Now, for his Ph.D. research, Abdelhalim is focusing on self-driving cars, where his work on the vehicle- to-infrastructure communications provided valuable experience. Both technologies rely on collecting vast quantities of traffic-related data and running experiments in simulation environments. Wanting to gain hands-on knowledge, Abdelhalim even joined Virginia Tech’s AutoDrive team: a student design team dedicated to developing their own self-driving car and representing Virginia Tech at the Society of Automotive Engineers’ (SAE International) AutoDrive Challenge. 

This image shows a traffic intersection simulation. A bus is traveling on Alumni Mall toward Main Street, with a car behind and another in front of it. On Main Street 11 vehicles are depicted.
A simulation of traffic at an intersection between Alumni Mall and Main Street in Blacksburg, Virginia. Image courtesy of Awad Abdelhalim.

    “It’s our responsibility as transportation engineers to think about how we’re going to integrate those robots into traffic by understanding how they really work,” he says.

    In some ways, that integration is a matter of engineering. Abdelhalim thinks self-driving cars could also be linked to traffic lights and to other autonomous vehicles. This would allow them to adjust their speed to opportunistically sync their arrival time at intersections just like the buses would. By speeding up or slowing down, they would affect the flow of traffic behind them, thus extending the benefits even to human drivers whose cars have no link with the system. Fuel is wasted the most from the stopping and restarting that cars undergo at intersections, so there’s a tremendous environmental reward to this technology as well.

    However, the biggest challenges concerning self-driving cars aren’t only engineering related.

“It’s also the regulations,” says Abdelhalim. “How do you regulate traffic when there is no driver to hold accountable?”  He continues, “because those cars are built on the assumption that they are going to reduce accidents to zero. . .they are held to a much higher public standard than a human driver.”

    The laws and regulations pertaining to traffic, which have been drafted and tweaked for decades, operate on the assumption that humans are the drivers. How does insurance handle a self-driving car hitting a human-driven car, or vice versa? Who is to blame if a self-driving car injures someone? What if they were jaywalking, or what if the front-seat occupant, who for safety reasons can override the autonomous car, wasn’t paying attention?

    When these difficult questions can be readily answered and the public can trust the technology, self-driving cars will be able to make their debut. According to Abdelhalim, because of the heavy investments in the technology by companies like Uber and Waymo, we will probably first see them being used as driverless taxis. Hopefully, as these cars ease their way onto the roads, the public will have time to adjust to the sight of empty cars driving alongside them.