TY - GEN
T1 - A Protocol for Mixed Autonomous and Human-Operated Vehicles at Intersections
AU - Sharon, Guni
AU - Stone, Peter
N1 - Publisher Copyright:
© Springer International Publishing AG. 2017.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Connected and autonomous vehicle technology has advanced rapidly in recent years. These technologies create possibilities for highly efficient, AI-based, transportation systems. One such system is the Autonomous Intersection Management (AIM), an intersection management protocol designed for the time when all vehicles are fully autonomous and connected. Experts, however, anticipate a long transition period during which human and autonomously operated vehicles will coexist. Unfortunately, AIM has been shown to provide little or no improvement over today’s traffic signals when less than 90% of the vehicles are autonomous, making AIM ineffective for a large portion of the transition period. This paper introduces a new protocol denoted Hybrid Autonomous Intersection Management (H-AIM), that is applicable as long as AIM is applicable and the infrastructure is able to sense approaching vehicles. Our experiments show that this protocol can decrease traffic delay for autonomous vehicles even at 1% technology penetration rate.
AB - Connected and autonomous vehicle technology has advanced rapidly in recent years. These technologies create possibilities for highly efficient, AI-based, transportation systems. One such system is the Autonomous Intersection Management (AIM), an intersection management protocol designed for the time when all vehicles are fully autonomous and connected. Experts, however, anticipate a long transition period during which human and autonomously operated vehicles will coexist. Unfortunately, AIM has been shown to provide little or no improvement over today’s traffic signals when less than 90% of the vehicles are autonomous, making AIM ineffective for a large portion of the transition period. This paper introduces a new protocol denoted Hybrid Autonomous Intersection Management (H-AIM), that is applicable as long as AIM is applicable and the infrastructure is able to sense approaching vehicles. Our experiments show that this protocol can decrease traffic delay for autonomous vehicles even at 1% technology penetration rate.
KW - Autonomous Intersection Management
KW - Autonomous vehicles
KW - Multiagent systems
UR - http://www.scopus.com/inward/record.url?scp=85036645418&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-71682-4_10
DO - 10.1007/978-3-319-71682-4_10
M3 - Conference contribution
AN - SCOPUS:85036645418
SN - 9783319716817
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 151
EP - 167
BT - Autonomous Agents and Multiagent Systems - AAMAS 2017 Workshops, Revised Selected Papers
A2 - Rodriguez-Aguilar, Juan A.
A2 - Sukthankar, Gita
PB - Springer Verlag
T2 - 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017
Y2 - 8 May 2017 through 12 May 2017
ER -
