RIM

Robust Intersection Management for Connected Autonomous Vehicles

Mohammad Khayatian, Mohammadreza Mehrabian, Aviral Shrivastava

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Utilizing intelligent transportation infrastructures can significantly improve the throughput of intersections of Connected Autonomous Vehicles (CAV), where an Intersection Manager (IM) assigns a target velocity to incoming CAVs in order to achieve a high throughput. Since the IM calculates the assigned velocity for a CAV based on the model of the CAV, it's vulnerable to model mismatches and possible external disturbances. As a result, IM must consider a large safety buffer around all CAVs to ensure a safe scheduling, which greatly degrades the throughput. In addition, IM has to assign a relatively lower speed to CAVs that intend to make a turn at the intersection to avoid rollover. This issue reduces the throughput of the intersection even more. In this paper, we propose a space and time-aware technique to manage intersections of CAVs that is robust against external disturbances and model mismatches. In our method, RIM, IM is responsible for assigning a safe Time of Arrival (TOA) and Velocity of Arrival (VOA) to an approaching CAV such that trajectories of CAVs before and inside the intersection does not conflict. Accordingly, CAVs are responsible for determining and tracking an optimal trajectory to reach the intersection at the assigned TOA while driving at VOA. Since CAVs track a position trajectory, the effect of bounded model mismatch and external disturbances can be compensated. In addition, CAVs that intend to make a turn at the intersection do not need to drive at a slow velocity before entering the intersection. Results from conducting experiments on a 1/10 scale intersection of CAVs show that RIM can reduce the position error at the expected TOA by 18X on average in presence of up to 10% model mismatch and an external disturbance with an amplitude of 5% of max range. In total, our technique can achieve 2.7X better throughput on average compared to velocity assignment techniques.

Original languageEnglish (US)
Title of host publicationProceedings - 39th IEEE Real-Time Systems Symposium, RTSS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages35-44
Number of pages10
ISBN (Electronic)9781538679074
DOIs
StatePublished - Jan 4 2019
Event39th IEEE Real-Time Systems Symposium, RTSS 2018 - Nashville, United States
Duration: Dec 11 2018Dec 14 2018

Publication series

NameProceedings - Real-Time Systems Symposium
Volume2018-December
ISSN (Print)1052-8725

Conference

Conference39th IEEE Real-Time Systems Symposium, RTSS 2018
CountryUnited States
CityNashville
Period12/11/1812/14/18

Fingerprint

Reaction injection molding
Managers
Throughput
Trajectories

Keywords

  • Connected Autonomous Vehicles
  • Cyber-Physical Systems
  • Traffic Intersection Management

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

Cite this

Khayatian, M., Mehrabian, M., & Shrivastava, A. (2019). RIM: Robust Intersection Management for Connected Autonomous Vehicles. In Proceedings - 39th IEEE Real-Time Systems Symposium, RTSS 2018 (pp. 35-44). [8603190] (Proceedings - Real-Time Systems Symposium; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RTSS.2018.00014

RIM : Robust Intersection Management for Connected Autonomous Vehicles. / Khayatian, Mohammad; Mehrabian, Mohammadreza; Shrivastava, Aviral.

Proceedings - 39th IEEE Real-Time Systems Symposium, RTSS 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 35-44 8603190 (Proceedings - Real-Time Systems Symposium; Vol. 2018-December).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Khayatian, M, Mehrabian, M & Shrivastava, A 2019, RIM: Robust Intersection Management for Connected Autonomous Vehicles. in Proceedings - 39th IEEE Real-Time Systems Symposium, RTSS 2018., 8603190, Proceedings - Real-Time Systems Symposium, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 35-44, 39th IEEE Real-Time Systems Symposium, RTSS 2018, Nashville, United States, 12/11/18. https://doi.org/10.1109/RTSS.2018.00014
Khayatian M, Mehrabian M, Shrivastava A. RIM: Robust Intersection Management for Connected Autonomous Vehicles. In Proceedings - 39th IEEE Real-Time Systems Symposium, RTSS 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 35-44. 8603190. (Proceedings - Real-Time Systems Symposium). https://doi.org/10.1109/RTSS.2018.00014
Khayatian, Mohammad ; Mehrabian, Mohammadreza ; Shrivastava, Aviral. / RIM : Robust Intersection Management for Connected Autonomous Vehicles. Proceedings - 39th IEEE Real-Time Systems Symposium, RTSS 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 35-44 (Proceedings - Real-Time Systems Symposium).
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abstract = "Utilizing intelligent transportation infrastructures can significantly improve the throughput of intersections of Connected Autonomous Vehicles (CAV), where an Intersection Manager (IM) assigns a target velocity to incoming CAVs in order to achieve a high throughput. Since the IM calculates the assigned velocity for a CAV based on the model of the CAV, it's vulnerable to model mismatches and possible external disturbances. As a result, IM must consider a large safety buffer around all CAVs to ensure a safe scheduling, which greatly degrades the throughput. In addition, IM has to assign a relatively lower speed to CAVs that intend to make a turn at the intersection to avoid rollover. This issue reduces the throughput of the intersection even more. In this paper, we propose a space and time-aware technique to manage intersections of CAVs that is robust against external disturbances and model mismatches. In our method, RIM, IM is responsible for assigning a safe Time of Arrival (TOA) and Velocity of Arrival (VOA) to an approaching CAV such that trajectories of CAVs before and inside the intersection does not conflict. Accordingly, CAVs are responsible for determining and tracking an optimal trajectory to reach the intersection at the assigned TOA while driving at VOA. Since CAVs track a position trajectory, the effect of bounded model mismatch and external disturbances can be compensated. In addition, CAVs that intend to make a turn at the intersection do not need to drive at a slow velocity before entering the intersection. Results from conducting experiments on a 1/10 scale intersection of CAVs show that RIM can reduce the position error at the expected TOA by 18X on average in presence of up to 10{\%} model mismatch and an external disturbance with an amplitude of 5{\%} of max range. In total, our technique can achieve 2.7X better throughput on average compared to velocity assignment techniques.",
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