TY - GEN
T1 - RIM
T2 - 39th IEEE Real-Time Systems Symposium, RTSS 2018
AU - Khayatian, Mohammad
AU - Mehrabian, Mohammadreza
AU - Shrivastava, Aviral
N1 - Funding Information:
This work was partially supported by funding from NIST Award 70NANB16H305, and by NSF grant CNS 1525855, and CPS 1645578.
Funding Information:
This work was partially supported by funding from NIST Award 70NANB16H305,and by NSF grant CNS 1525855, and CPS 1645578.
Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/4
Y1 - 2019/1/4
N2 - 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.
AB - 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.
KW - Connected Autonomous Vehicles
KW - Cyber-Physical Systems
KW - Traffic Intersection Management
UR - http://www.scopus.com/inward/record.url?scp=85061543246&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061543246&partnerID=8YFLogxK
U2 - 10.1109/RTSS.2018.00014
DO - 10.1109/RTSS.2018.00014
M3 - Conference contribution
AN - SCOPUS:85061543246
T3 - Proceedings - Real-Time Systems Symposium
SP - 35
EP - 44
BT - Proceedings - 39th IEEE Real-Time Systems Symposium, RTSS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 December 2018 through 14 December 2018
ER -