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Crossroads: Autonomous Intersection Management

Connected, automated and electric vehicles
Modeling and simulation

Partially funded by National Science Foundation grants CCF 1055094 (CAREER), CNS 1525855, NIST 60NANB16D305, & NIST 60NANB15D322


The introduction and adoption of autonomous vehicles will require intelligent autonomous intersection management. Intersection management includes maintaining a safety buffer around the vehicle while factoring variable network and computational time delays. Modeling these network and computational delays can degrade the throughput of the intersection even when processed by a state-of-the-art intersection manager.


To combat these limitations, ASU researchers have developed Crossroads, a time-sensitive method to program the interface of a vehicle and intersection that does not require an additional buffer to account for network and computational delays.

Using a small-scale intersection model with radio control cars, the Crossroads approach demonstrated a reduction in average vehicle wait time at a single-lane intersection of 24 percent. Crossroads obviates the need for large buffers to accommodate network and computational delays.

Simulated Intersection
A small-scale (“1/10 scale”) model of an intersection with TRAXXAS radio control cars



Through extensive MATLAB simulations, Crossroads has shown increased throughput compared to existing approaches: 1.62X higher throughput (on average) than Velocity Transaction Intersection Management (VT-IM) with extra safety buffers, and 1.36X higher throughput (on average) than autonomous intersection management (AIM).

Crossroads performance compared to AIM and VT-IM


For more information, including publication in the 54th-annual Design Automation Conference (DAC), see

ASU Leads

Aviral Shrivastava

Associate Professor, School of Computing, Informatics, and Decision Systems Engineering