Pay to change lanes: A cooperative lane-changing strategy for connected/automated driving

Lin, Dianchao; Li, Li; Jabari, Saif Eddin

doi:10.1016/j.trc.2019.06.006

Cited by 68 publications

(32 citation statements)

References 20 publications

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“…A combined cost function includes both non-cooperative and cooperative costs for connected vehicles when regarding the latter as the extra cost of the control action [33] . A recent study also considered possible charges for selfish LC behaviors (like changing lanes in an over-crowded road section) to reduce the congestion designed for connected vehicles [34].…”

Section: Hybrid (User and Social-optimal) Modelsmentioning

confidence: 99%

Estimating the Social Gap With a Game Theory Model of Lane Changing

Levinson

2021

IEEE Trans. Intell. Transport. Syst.

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Changing lanes is a commonly-used technique for drivers to either overtake slow-moving cars or enter/exit highway ramps. Optional lane changes may save drivers travel time but increase the risk of collision with others. Drivers make such decisions based on experience and emotion rather than analysis, and thus may fail to select the best solution while in a dynamic state of flux. Unlike human drivers, autonomous vehicles can systematically analyze their surroundings and make real-time decisions accordingly. This paper develops a game theory-based lane-changing model by comparing two types of optimization methods. To realize our expectations, we need to first investigate the payoff function of drivers in discretionary lane-changing maneuvers and then quantify it in an equation of costs that trades-off safety and time-saving. After the evaluation for each alternative strategy combination, the results show that there exists a social gap in the discretionary lane-changing game. To deal with that problem, we provide some suggestions for future policy as well as autonomous vehicle controller designs, offering solutions to reduce the impact of disturbances and crashes caused by inappropriate lane changes, and also, inspire further research about more complex cases.

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Section: Hybrid (User and Social-optimal) Modelsmentioning

confidence: 99%

Estimating the Social Gap With a Game Theory Model of Lane Changing

Levinson

2021

IEEE Trans. Intell. Transport. Syst.

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“…Lombard et al (2017) propose an approach based on cooperative V2V negotiation. In the same vein, the method presented by Lin et al (2019) consists in the use of transferable utility games, in which gaps in traffic are created in exchange for monetary compensation. In other words, vehicles can pay to change lane faster and reduce their travel time.…”

Section: Lane Change and Mergementioning

confidence: 99%

Connectivity and Automation as Enablers for Energy-Efficient Driving and Road Traffic Management

Othman

Nunzio

Sciarretta

et al. 2021

Handbook of Climate Change Mitigation and Adaptation

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In the member countries of the Organization for Economic Cooperation and Development (OECD), projections show that the improved energy efficiency in transportation may lead to a net decline of about 2% in energy use until 2040, thus outpacing the predicted increase of vehicle-miles traveled (VMT). However, in OECD-Europe, transportation still represents the biggest source of carbon emissions, contributing by about 25% to the total CO2 emissions, with cars and vans representing more than two thirds of this share. The situation is even more alarming in non-OECD countries, where the transportation energy demand is expected to rise by 64% until 2040. The shift that we are witnessing toward the adoption of connected and automated vehicles (CAVs) is going to be perhaps, the most disruptive since the early days of automobiles and could revolutionize movement of people and goods. This level of connectivity and autonomy will transform transportation in several dimensions with important societal and economic impacts: improved safety, increased comfort, time saving potential, and more efficient road utilization are among the most widely discussed positive impacts of CAVs. However, the potential energy efficiency benefits of these technologies remain uncertain. From a single-vehicle efficiency perspective, research suggests that lightweight, low-speed, autonomous vehicles have the potential to achieve fuel economy an order of magnitude higher than current cars. Yet, at system-wide level, current estimates suggest that the total energy consumption impacts can range from a 90% decrease to a 200% increase in fuel consumption as compared to a projected 2050 baseline energy consumption. The paradigm that traffic congestion mitigation should reduce CO2 emissions is yet to be proved. Therefore, interest in transportation regulation problems with explicit environmental considerations is growing. This work takes a more in-depth look at increased opportunities for energy-efficient driving with energy-oriented traffic management and CAVs deployment. In particular, the focus will be put on the road traffic control strategies in urban networks using connectivity to enable variable speed limits and traffic light adaptive control, as well as the energy-saving opportunities that arise for individual CAVs by anticipating future road geometry, traffic conditions, and interactions with neighboring vehicles.

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“…Lin et al . [27] treated lane changing as transferable utility games with side payments, and vehicles could exchange right‐of‐way for money. Results showed that vehicles with low and high values of travel time derived benefit from the proposed approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Cooperative bypassing algorithm for connected and autonomous vehicles in mixed traffic

Feng

et al. 2020

IET Intelligent Transport Systems

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Pay to change lanes: A cooperative lane-changing strategy for connected/automated driving

Cited by 68 publications

References 20 publications

Estimating the Social Gap With a Game Theory Model of Lane Changing

Estimating the Social Gap With a Game Theory Model of Lane Changing

Connectivity and Automation as Enablers for Energy-Efficient Driving and Road Traffic Management

Cooperative bypassing algorithm for connected and autonomous vehicles in mixed traffic

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