A reservation-based multiagent system for intersection control

Dresner, Kurt; Stone, Peter

doi:10.1016/s1474-6670(17)31964-x

Cited by 9 publications

(12 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where γ ≥ 1 and t min i is the minimum feasible travel time CAV i can reach the intersection considering (5) and (6). The value γ = 1 minimizes the total travel time but maximizes the fuel consumption, while for increasing γ the total travel time of vehicles increases and the total fuel consumption decreases as vehicles choose from a larger family of trajectories.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Having obtainedJ i (t m i ), ∀i ∈ N , the next step is to solve a relaxed version of problem (11) to derive close-to-optimal values for t m i . Towards this direction we relax the rear-end collision constraints (4) to hold only in the MZ rather than both the CZ and MZ, yielding constraint (5). In this way, we eliminate dependence of the optimization problem from the trajectories .…”

Section: B Phasementioning

confidence: 99%

“…A popular centralized heuristic method uses the concept of reservations, where the intersection is divided into cells that are assigned to a specific vehicle for a certain amount of time [5]. This is a rule-based method which is simple to implement and computationally fast; nonetheless, it does not utilize rigorous mathematical tools to optimize performance and does not account for the vehicle dynamics.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing the trade-off between fuel consumption and travel time in an unsignalized autonomous intersection crossing

Hadjigeorgiou

Timotheou

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

View full text Add to dashboard Cite

Connected and autonomous vehicles (CAVs) have the potential to disrupt road transportation. CAVs provide several attractive features, such as seamless connectivity and fine-grained control, which can be exploited to improve the efficiency of traffic networks. In this work, the problem of CAV coordination at an unsignalized intersection crossing is considered, aiming to select the CAV trajectories that minimize fuel consumption and/or travel time. Nonetheless, the minimization of travel time implies high fuel consumption and vice-versa. For this reason, this work considers the problem of simultaneously optimizing the fuel consumption-travel time trade-off for a set of CAVs that are expected to arrive at the intersection within a specific time-window. As the resulting problem is non-convex, we construct a Mixed-Integer Programming formulation that provides tight lower and upper bounds. We also develop a heuristic convex-concave procedure that yields fast, highquality solutions. Simulation results validate the effectiveness of the proposed approaches and highlight the importance of optimizing the fuel consumption-travel time trade-off, as small compromises in travel time produce significant fuel savings.

show abstract

Section: Problem Formulationmentioning

confidence: 99%

Section: B Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing the trade-off between fuel consumption and travel time in an unsignalized autonomous intersection crossing

Hadjigeorgiou

Timotheou

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

View full text Add to dashboard Cite

show abstract

“…In the literature, several studies on AIM focus on different aspects. For example, Dresner and Stone ( 12 ) proposed a reservation-based intersection control mechanism that assumes that fully automated vehicles cross the intersection with a fixed velocity. According to this mechanism, each intersection is divided into a grid of space–time tiles to monitor potential conflicts.…”

mentioning

confidence: 99%

“…AIM ( 12 ) deals with the scenario in which all the vehicles under coordination are AVs, raising potential issues because of the lack of compatibility with conventional vehicles. To address this problem, Dresner and Stone ( 25 ) proposed a method in which a green light is periodically provided to human-driven vehicles.…”

mentioning

confidence: 99%

Optimal Control of Automated Vehicles for Autonomous Intersection Management With Design Specifications

Wang

Mahlberg

Levin

2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

In this article, we study optimal control of automated vehicles (AVs) at an autonomous intersection with design specifications, where the intersection manager (computer) controls the acceleration/deceleration of AVs within a control region to determine their conflict-free trajectories. We develop a mathematical model to describe the cooperative dynamics of vehicles within the control zone of a signal-free intersection. Further, we formulate an optimization problem with the objective of maximizing traffic throughput while minimizing passenger discomfort. We then address the problem using mathematical programming to yield the optimal control input for each vehicle inside the control zone. We apply the proposed control mechanism for optimally coordinating the movement of AVs at an autonomous intersection subject to physical and safety constraints. In other words, the solution, if implemented, would allow vehicles to pass the intersection with safety guarantees (avoiding collisions), high-level efficiency (maximizing throughput), and good comfort (minimizing discomfort) without deploying traffic lights. In addition, we derive analytical specifications for the design and deployment of autonomous intersections, which appears to have been largely ignored in prior studies. Finally, we conduct a series of numerical experiments to show the effectiveness of the methodology proposed in this article. As the framework developed here is fairly general and can be extended to cover many complex traffic scenarios, we believe that the procedure presented in this article will be useful with the advent of AVs in the near future.

show abstract

Mobility as a Resource (MaaR) for Resilient Human-Centric Automation – A Vision Paper

Waller,

Polydoropoulou,

Tassiulas

et al. 2024

Data Sci. Transp.

View full text Add to dashboard Cite

With technological advances, mobility has been moving from a product (i.e., traditional modes and vehicles), to a service (i.e., Mobility as a Service, MaaS). However, as observed in other fields (e.g., cloud computing resource management) we argue that mobility will evolve from a service to a resource (i.e., “Mobility as a Resource”, MaaR). Further, due to increasing scarcity of shared-mobility spaces across traditional and emerging modes, the transition must be viewed within the critical need for ethical and equitable solutions for the traveling public (i.e., research is needed to avoid hyper-market driven outcomes for society). The evolution of mobility into a resource requires novel conceptual frameworks, technologies, processes and perspectives of analysis. A key component of the future MaaR system is the technological capacity to observe, allocate and manage (in real-time) the smallest envisionable units of mobility (i.e., atomic units of mobility capacity) while providing prioritized attention to human movement and ethical metrics related to access, consumption and impact. To facilitate research into the envisioned future system, this paper proposes initial frameworks which synthesize and advance methodologies relating to highly dynamic capacity reservation systems. Future research requires synthesis across transport network management, demand behavior, mixed-mode usage, and equitable mobility.

show abstract

A reservation-based multiagent system for intersection control

Cited by 9 publications

References 2 publications

Optimizing the trade-off between fuel consumption and travel time in an unsignalized autonomous intersection crossing

Optimizing the trade-off between fuel consumption and travel time in an unsignalized autonomous intersection crossing

Optimal Control of Automated Vehicles for Autonomous Intersection Management With Design Specifications

Mobility as a Resource (MaaR) for Resilient Human-Centric Automation – A Vision Paper

Contact Info

Product

Resources

About