Control of connected and automated vehicles: State of the art and future challenges

In this paper, we provide a decentralized theoretical framework for coordination of connected and automated vehicles (CAVs) in different traffic scenarios. The framework includes: (1) an upper-level optimization that yields for each CAV its optimal path, including the time, to pass through a given traffic scenario while alleviating congestion; and (2) a low-level optimization that yields for each CAV its optimal control input (acceleration/deceleration) to achieve the optimal path and time derived in the upper-level. We provide a complete, analytical solution of the low-level optimization problem that includes the rear-end safety constraint, where the safe distance is a function of speed, in addition to the state and control constraints. Furthermore, we provide a geometric duality framework using hyperplanes to prove strong duality of the upper-level optimization problem. The latter implies that the optimal path and time for each CAV does not activate any of the state, control, and safety constraints of the low-level optimization, thus allowing for online implementation. We validate the effectiveness of the proposed theoretical framework through simulation.

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“…A detailed discussion of the research efforts that have been reported in the literature to date in this area can be found in [28] and [37].…”

Section: A Related Workmentioning

confidence: 99%

Optimal Path Planning for Connected and Automated Vehicles at Urban Intersections

Malikopoulos

Zhao

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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“…For the eco-routing case, we only show the results for solving (4). In other words, we only solve the eco-routing problem for conventional vehicles using the energy model discussed in Section III-A.…”

Section: Non-cav Flow Modelingmentioning

confidence: 99%

The Penetration Rate Effect of Connected and Automated Vehicles in Mixed Traffic Routing

Houshmand

Wollenstein-Betech

Cassandras

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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We study the problem of routing Connected and Automated Vehicles (CAVs) in the presence of mixed traffic (coexistence of regular vehicles and CAVs). In this setting, we assume that all CAVs belong to the same fleet, and can be routed using a centralized controller. The routing objective is to minimize a given overall fleet traveling cost (travel time or energy consumption). We assume that regular vehicles (non-CAVs) choose their routing decisions selfishly to minimize their traveling time. We propose an algorithm that deals with the routing interaction between CAVs and regular uncontrolled vehicles. We investigate the effect of assigning system-centric routes under different penetration rates (fractions) of CAVs. To validate our method, we apply the proposed routing algorithms to the Braess Network and to a sub-network of the Eastern Massachusetts (EMA) transportation network using actual traffic data provided by the Boston Region Metropolitan Planning Organization. The results suggest that collaborative routing decisions of CAVs improve not only the cost of CAVs, but also that of the non-CAVs. Furthermore, even a small CAV penetration rate can ease congestion for the entire network.

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“…Lately, a decentralized optimal control framework was presented for coordinating online CAVs in different transportation scenarios, yet without considering state and control constraints; see Ntousakis et al (2016); Zhao et al (2019), or without considering rear-end collision avoidance constraint; see Malikopoulos et al (2018). A detailed discussion of the research efforts in this area can be found in recent survey papers (Rios-Torres and Malikopoulos, 2017;Guanetti et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Decentralized optimal coordination of connected and automated vehicles for multiple traffic scenarios

2020

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Connected and automated vehicles (CAVs) provide the most intriguing opportunity to optimize energy consumption and travel time. Several approaches have been proposed in the literature that allow CAVs to coordinate in situations where there is a potential conflict, for example, in signalized intersections, merging at roadways and roundabouts, to reduce energy consumption and optimize traffic flow. In this paper, we consider the problem of coordinating CAVs in a corridor consisting of multiple traffic scenarios. We formulate a two-level optimization problem in which we maximize traffic throughput in the upper-level problem, and derive a closed-form analytical solution that yields the optimal control input for each CAV, in terms of fuel consumption, in the low-level problem. We validate the effectiveness of the solution through simulation under 100% CAV penetration rate. Fuel consumption and travel time for the vehicles are significantly reduced compared to a baseline scenario consisting of human-driven vehicles.

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Control of connected and automated vehicles: State of the art and future challenges

Cited by 502 publications

References 211 publications

Optimal Path Planning for Connected and Automated Vehicles at Urban Intersections

Optimal Path Planning for Connected and Automated Vehicles at Urban Intersections

The Penetration Rate Effect of Connected and Automated Vehicles in Mixed Traffic Routing

Decentralized optimal coordination of connected and automated vehicles for multiple traffic scenarios

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