TY - GEN
T1 - Roadrunner
T2 - 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020
AU - Wang, Michael I.C.
AU - Wang, Jiacheng
AU - Wen, Charles H.P.
AU - Chao, H. Jonathan
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/20
Y1 - 2020/9/20
N2 - The advanced controls of Connected Autonomous Vehicles (CAVs) have enabled complex traffic management for higher efficiency and safety, such as dynamic lane assignment and non-signalized autonomous intersection management (AIM). Unlike traditional intersections, non-signalized AIM does not rely on the settings of signal phases or traffic-light cycles for controlling the traffic, and the measurements of lane congestion can no longer be based on these two settings. Moreover, in the CAV environment, lane use can be more dynamic in response to real-time traffic demands and the policy of AIM. Therefore, we proposed a new AIM system (named Roadrunner) that combines dynamic lane assignment and autonomous intersection management. In Roadrunner, lane use is not limited by turns, and lanes are dynamically assigned to CAVs, based on our lane-assignment policy and a novel approach of measuring lane congestion levels over non-signalized intersection controls. We implemented Roadrunner in a SUMO simulator and compared the performance with different intersection management systems, where lane use is predefined. The result shows that Roadrunner increases the intersection capacity by more than 11%, and CAVs have lower average traveling delay.
AB - The advanced controls of Connected Autonomous Vehicles (CAVs) have enabled complex traffic management for higher efficiency and safety, such as dynamic lane assignment and non-signalized autonomous intersection management (AIM). Unlike traditional intersections, non-signalized AIM does not rely on the settings of signal phases or traffic-light cycles for controlling the traffic, and the measurements of lane congestion can no longer be based on these two settings. Moreover, in the CAV environment, lane use can be more dynamic in response to real-time traffic demands and the policy of AIM. Therefore, we proposed a new AIM system (named Roadrunner) that combines dynamic lane assignment and autonomous intersection management. In Roadrunner, lane use is not limited by turns, and lanes are dynamically assigned to CAVs, based on our lane-assignment policy and a novel approach of measuring lane congestion levels over non-signalized intersection controls. We implemented Roadrunner in a SUMO simulator and compared the performance with different intersection management systems, where lane use is predefined. The result shows that Roadrunner increases the intersection capacity by more than 11%, and CAVs have lower average traveling delay.
UR - http://www.scopus.com/inward/record.url?scp=85099667432&partnerID=8YFLogxK
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U2 - 10.1109/ITSC45102.2020.9294688
DO - 10.1109/ITSC45102.2020.9294688
M3 - Conference contribution
AN - SCOPUS:85099667432
T3 - 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
BT - 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 September 2020 through 23 September 2020
ER -