Development of a signal-head-free intersection control logic in a fully connected and autonomous vehicle environment. (July 2018)

• Record Type: Journal Article
• Authors: Mirheli, Amir; Hajibabai, Leila; Hajbabaie, Ali
• Is Part Of: Transportation research. Volume 92(2018)
• Page Start: 412
• Page End: 425
• Publication Date: 2018-07
• Subjects: Control logic -- Autonomous intersection control -- Monte Carlo tree search -- Look-ahead model -- Connected and autonomous vehicles -- Dynamic programming; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2018.04.026
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Development of a signal-head-free intersection control logic for connected and autonomous vehicles. Dynamic optimization of acceleration rates to maximize intersection throughput with safe trajectories. Development of a dynamic programming technique incorporating Monte Carlo tree search algorithm. Travel time reductions of 59.4%–83.7% compared to fixed-time and fully-actuated control case studies. Potential to eliminate the number of near-crash instances to zero in case studies. Abstract: Establishment of effective cooperation between vehicles and transportation infrastructure improves travel reliability in urban transportation networks. Lack of collaboration, however, exacerbates congestion due mainly to frequent stops at signalized intersections. It is beneficial to develop a control logic that collects basic safety message from approaching connected and autonomous vehicles and guarantees efficient intersection operations with safe and incident free vehicle maneuvers. In this paper, a signal-head-free intersection control logic is formulated into a dynamic programming model that aims to maximize the intersection throughput. A stochastic look-ahead technique is proposed based on Monte Carlo tree search algorithm to determine the near-optimal actions (i.e., acceleration rates) over time to prevent movement conflicts. Our numerical results confirm that the proposed technique can solve the problem efficiently and addresses the consequences of existing traffic signals. The proposed approach, while completely avoids incidents at intersections, significantly reduces travel time (ranging between 59.4% and 83.7% when compared to fixed-time and fully-actuated control strategies) at intersections under various demand patterns.