Helping autonomous vehicles navigate tricky highway merges

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If autonomous autos are ever going to attain widespread adoption, we have to know they’re able to navigating advanced visitors conditions, resembling merging into heavy visitors when lanes disappear on a freeway. To that finish, researchers from North Carolina State University have developed a way that enables autonomous car software program to make the related calculations extra rapidly—enhancing each visitors and security in simulated autonomous car programs.

“Right now, the programs designed to help autonomous vehicles navigate lane changes rely on making problems computationally simple enough to resolve quickly, so the vehicle can operate in real time,” says Ali Hajbabaie, corresponding creator of a paper on the work and an assistant professor of civil, development and environmental engineering at NC State. “Nevertheless, simplifying the issue an excessive amount of can really create a brand new set of issues, since actual world situations are not often easy.

“Our approach allows us to embrace the complexity of real-world problems. Rather than focusing on simplifying the problem, we developed a cooperative distributed algorithm. This approach essentially breaks a complex problem down into smaller sub-problems, and sends those to different processors to solve separately. This process, called parallelization, improves efficiency significantly.”

At this level, the researchers have solely examined their strategy in simulations, the place the sub-problems are shared amongst totally different cores in the identical computing system. Nevertheless, if autonomous autos ever use the strategy on the highway, the autos would community with one another and share the computing sub-problems.

In proof-of-concept testing, the researchers checked out two issues: whether or not their method allowed autonomous vehicle software program to resolve merging issues in actual time; and the way the brand new “cooperative” strategy affected visitors and security in comparison with an present mannequin for navigating autonomous autos.

When it comes to computation time, the researchers discovered their strategy allowed autonomous vehicles to navigate advanced freeway lane merging situations in actual time in average and heavy traffic, with spottier efficiency when visitors volumes obtained notably excessive.

However when it got here to enhancing visitors and security, the brand new method did exceptionally nicely. In some situations, notably when traffic quantity was decrease, the 2 approaches carried out about the identical. However in most cases, the brand new strategy outperformed the earlier mannequin by a substantial margin. What’s extra, the brand new method had zero incidents the place autos needed to come to a cease or the place there have been “near crash conditions.” The opposite mannequin’s outcomes included a number of situations the place there have been actually hundreds of stoppages and close to crash situations.

“For a proof-of-concept test, we’re very pleased with how this technique has performed,” Hajbabaie says. “There may be room for enchancment, however we’re off to an ideal begin.

“The good news is that we’re developing these tools and tackling these problems now, so that we’re in a good position to ensure safe autonomous systems as they are adopted more widely.”

The paper, “Distributed Cooperative Trajectory and Lane changing Optimization of Connected Automated Vehicles: Freeway Segments with Lane Drop,” seems within the journal Transportation Research Half C. First creator of the paper is Mehrdad Tajalli, a latest Ph.D. graduate of NC State. The paper was co-authored by Ramin Niroumand, a Ph.D. scholar at NC State.


Researchers find way to make traffic models more efficient


Extra data:
Mehrdad Tajalli et al, Distributed cooperative trajectory and lane altering optimization of related automated autos: Freeway segments with lane drop, Transportation Research Half C: Rising Applied sciences (2022). DOI: 10.1016/j.trc.2022.103761

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Serving to autonomous autos navigate tough freeway merges (2022, August 24)
retrieved 24 August 2022
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