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Helping Autonomous Vehicles Navigate Tricky Highway Merges

aerial view of a complex highway interchange featuring more than a dozen lanes of traffic going in half a dozen directions
Photo credit: Nick Fewings.

For Immediate Release

Ali Hajbabaie

If autonomous vehicles are ever going to achieve widespread adoption, we need to know they are capable of navigating complex traffic situations, such as merging into heavy traffic when lanes disappear on a highway. To that end, researchers from North Carolina State University have developed a technique that allows autonomous vehicle software to make the relevant calculations more quickly – improving both traffic and safety in simulated autonomous vehicle systems.

“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 author of a paper on the work and an assistant professor of civil, construction and environmental engineering at NC State. “However, simplifying the problem too much can actually create a new set of problems, since real world scenarios are rarely simple.

“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 point, the researchers have only tested their approach in simulations, where the sub-problems are shared among different cores in the same computing system. However, if autonomous vehicles ever use the approach on the road, the vehicles would network with each other and share the computing sub-problems.

In proof-of-concept testing, the researchers looked at two things: whether their technique allowed autonomous vehicle software to solve merging problems in real time; and how the new “cooperative” approach affected traffic and safety compared to an existing model for navigating autonomous vehicles.

In terms of computation time, the researchers found their approach allowed autonomous vehicles to navigate complex freeway lane merging scenarios in real time in moderate and heavy traffic, with spottier performance when traffic volumes got particularly high.

But when it came to improving traffic and safety, the new technique did exceptionally well. In some scenarios, particularly when traffic volume was lower, the two approaches performed about the same. But in most instances, the new approach outperformed the previous model by a considerable margin. What’s more, the new technique had zero incidents where vehicles had to come to a stop or where there were “near crash conditions.” The other model’s results included multiple scenarios where there were literally thousands of stoppages and near crash conditions.

“For a proof-of-concept test, we’re very pleased with how this technique has performed,” Hajbabaie says. “There is room for improvement, but we’re off to a great start.

“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,” appears in the journal Transportation Research Part C. First author of the paper is Mehrdad Tajalli, a recent PhD graduate of NC State. The paper was co-authored by Ramin Niroumand, a PhD student at NC State.

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Note to Editors: The study abstract follows.

“Distributed Cooperative Trajectory and Lane changing Optimization of Connected Automated Vehicles: Freeway Segments with Lane Drop”

Authors: Mehrdad Tajalli, Ramin Niroumand and Ali Hajbabaie, North Carolina State University

Published: Aug. 20, Transportation Research Part C

DOI: 10.1016/j.trc.2022.103761

Abstract: This study presents a methodology for optimal control of connected automated vehicles (CAVs) in freeway segments with a lane drop. Lane drops can create bottlenecks with a considerable number of mandatory and discretionary lane-changing maneuvers when traffic volume is high, which can eventually lead to stop-and-go conditions. Proper motion planning aligned with optimal lane changing upstream of a lane drop can increase capacity and reduce the number of stops and the risk of collision. This paper introduces a vehicle-level mixed-integer program to control longitudinal and lateral movement of CAVs, provide a smooth flow of traffic, and avoid congestion in freeway segments with lane drops. To ensure the feasibility of vehicle-level decisions and promote system-level optimality, a cooperative distributed algorithm is established, where CAVs coordinate their decisions to find the optimal longitudinal and lateral maneuvers that avoid collisions among all vehicles. The proposed coordination scheme lets CAVs find their optimal trajectories based on predictive information from surrounding vehicles (i.e., future locations and speeds) and coordinate their lane-changing decisions to avoid collisions. The results show that optimal lane changing of CAVs smoothens the traffic flow and increases freeway capacity in congested traffic conditions. Compared with all-knowing CAVs simulated in Vissim, the proposed methodology reduced the average travel time by up to 86.4%. It increased the number of completed trips by up to 134.3% based on various traffic demands and lane drop layout combinations.

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