TY - JOUR
T1 - Crossroads+
T2 - A time-aware approach for intersection management of connected autonomous vehicles
AU - Khayatian, Mohammad
AU - Lou, Yingyan
AU - Mehrabian, Mohammadreza
AU - Shirvastava, Aviral
N1 - Funding Information:
This work was partially supported by funding from NIST Award No. 70NANB19H144 and by National Science Foundation Grants No. CNS 1525855, No. CPS 1645578, and No. CCF 172346—the NSF/Intel joint research center for Computer Assisted Programming for Heterogeneous Architectures (CAPA). Authors’ addresses: M. Khayatian, Y. Lou, M. Mehrabian, and A. Shirvastava, Arizona State Univeristy, 660 South Mill Avenue, Tempe, AZ, USA; emails: {mkhayati, Yingyan.Lou, mmehrabi, aviral.shrivastava}@asu.edu. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2019 Association for Computing Machinery. 2378-962X/2019/11-ART20 $15.00 https://doi.org/10.1145/3364182
Publisher Copyright:
© 2019 Association for Computing Machinery. All rights reserved.
PY - 2019/11
Y1 - 2019/11
N2 - As vehicles become autonomous and connected, intelligent management techniques can be utilized to operate an intersection without a traffic light. When a Connected Autonomous Vehicle (CAV) approaches an intersection, it shares its status and intended direction with the IntersectionManager (IM), and the IM checks the status of other CAVs and assigns a target velocity/reference trajectory for it to maintain. In practice, however, there is an unknown delay between the time a CAV sends a request to the IMand the moment it receives back the response, namely, the Round-Trip Delay (RTD). As a result, the CAV will start tracking the target velocity/reference trajectory later than when the IM expects, which may lead to accidents. In this article, we present a time-aware approach, Crossroads+, that makes CAVs' behaviors deterministic despite the existence of the unknown RTD. In Crossroads+, we use timestamping and synchronization to ensure that both the IM and the CAVs have the same notion of time. The IM will also set a fixed start time to track the target velocity/ reference trajectory for each CAV. The effectiveness of the proposed Crossroads+ technique is illustrated by experiments on a 1/10 scale model of an intersection with CAVs.We also built a simulator to demonstrate the scalability of Crossroads+ for multi-lane intersections. Results from our experiments indicate that our approach can reduce the position uncertainty by 15% in comparison with conventional techniques and achieve up to 36% better throughputs.
AB - As vehicles become autonomous and connected, intelligent management techniques can be utilized to operate an intersection without a traffic light. When a Connected Autonomous Vehicle (CAV) approaches an intersection, it shares its status and intended direction with the IntersectionManager (IM), and the IM checks the status of other CAVs and assigns a target velocity/reference trajectory for it to maintain. In practice, however, there is an unknown delay between the time a CAV sends a request to the IMand the moment it receives back the response, namely, the Round-Trip Delay (RTD). As a result, the CAV will start tracking the target velocity/reference trajectory later than when the IM expects, which may lead to accidents. In this article, we present a time-aware approach, Crossroads+, that makes CAVs' behaviors deterministic despite the existence of the unknown RTD. In Crossroads+, we use timestamping and synchronization to ensure that both the IM and the CAVs have the same notion of time. The IM will also set a fixed start time to track the target velocity/ reference trajectory for each CAV. The effectiveness of the proposed Crossroads+ technique is illustrated by experiments on a 1/10 scale model of an intersection with CAVs.We also built a simulator to demonstrate the scalability of Crossroads+ for multi-lane intersections. Results from our experiments indicate that our approach can reduce the position uncertainty by 15% in comparison with conventional techniques and achieve up to 36% better throughputs.
KW - Connected autonomous vehicles
KW - Cyber-physical systems
KW - Intersection management
KW - Round-trip delay
UR - http://www.scopus.com/inward/record.url?scp=85075638662&partnerID=8YFLogxK
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U2 - 10.1145/3364182
DO - 10.1145/3364182
M3 - Article
AN - SCOPUS:85075638662
SN - 2378-962X
VL - 4
JO - ACM Transactions on Cyber-Physical Systems
JF - ACM Transactions on Cyber-Physical Systems
IS - 2
M1 - 20
ER -