A Survey on the Coordination of Connected and Automated Vehicles at Intersections and Merging at Highway On-Ramps

Rios-Torres, Jackeline; Malikopoulos, Andreas A.

doi:10.1109/tits.2016.2600504

Cited by 686 publications

(305 citation statements)

References 80 publications

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“…Without a clear signal, such as a traffic light, the robot must make a decision based on the behavior of surrounding vehicles. Current approaches to solve this problem employ the use of a vehicle-to-vehicle communication systems alongside with a reservation based approach [17]. However such approaches were developed with automated vehicles in mind, and would require equipping all vehicles with such a system to function properly.…”

Section: Introductionmentioning

confidence: 99%

“…In such scenarios, the behavior of the vehicle is dependent on the action of surrounding agents. Several approaches have targeted the area of vehicle coordination to enable smooth interactions in intersection and merging scenarios [17]. Campos et al present a decentralized solution for intersection crossing where local state constraints are used to enforce collision avoidance [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Why did the robot cross the road? — Learning from multi-modal sensor data for autonomous road crossing

Radwan

Winterhalter

Dornhege

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Why did the robot cross the road? — Learning from multi-modal sensor data for autonomous road crossing

Radwan

Winterhalter

Dornhege

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…(iii) If considering more than one route for an OD pair, then the route flows cannot be uniquely determined by solving the TAP (2), thus leading to unstable route-choice probabilities, which would undermine the accuracy of the approximation to the Jacobian matrix in (17).…”

Section: Remarkmentioning

confidence: 99%

“…First, by taking advantage of the rapid emergence of Connected Automated Vehicles (CAVs) [14,15,16,17], it has become feasible to automate routing decisions, thus solving a system-centric forward problem in which all CAVs (bypassing driver decisions) cooperate to optimize the overall system performance. Second, we propose a modification to existing GPS navigation algorithms recommending to all drivers socially optimal routes.…”

Section: Introductionmentioning

confidence: 99%

The price of anarchy in transportation networks by estimating user cost functions from actual traffic data

Zhang

Pourazarm

Cassandras

et al. 2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Among the many functions a Smart City must support, transportation dominates in terms of resource consumption, strain on the environment, and frustration of its citizens. We study transportation networks under two different routing policies, the commonly assumed selfish user-centric routing policy and a socially-optimal system-centric one. We consider a performance metric of efficiency -the Price of Anarchy (PoA) -defined as the ratio of the total travel latency cost under selfish routing over the corresponding quantity under socially-optimal routing. We develop a data-driven approach to estimate the PoA, which we subsequently use to conduct a case study using extensive actual traffic data from the Eastern Massachusetts road network. To estimate the PoA, our approach learns from data a complete model of the transportation network, including origin-destination demand and user preferences. We leverage this model to propose possible strategies to reduce the PoA and increase efficiency.

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“…It uses its on-board sensors to detect the ambient environment and regulate the speeds of the vehicle to increase ride comfort. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications enable the so-called connected automated vehicles (CAVs), extending the visibility of automated vehicles [8]. When a group of CAVs travel with short intervehicle headway or gap, a platoon is formed [9].…”

Section: Introductionmentioning

confidence: 99%

A Robust Longitudinal Control Strategy of Platoons under Model Uncertainties and Time Delays

Chen

Wang

Alkim

et al. 2018

Journal of Advanced Transportation

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Automated vehicles are designed to free drivers from driving tasks and are expected to improve traffic safety and efficiency when connected via vehicle-to-vehicle communication, that is, connected automated vehicles (CAVs). The time delays and model uncertainties in vehicle control systems pose challenges for automated driving in real world. Ignoring them may render the performance of cooperative driving systems unsatisfactory or even unstable. This paper aims to design a robust and flexible platooning control strategy for CAVs. A centralized control method is presented, where the leader of a CAV platoon collects information from followers, computes the desired accelerations of all controlled vehicles, and broadcasts the desired accelerations to followers. The robust platooning is formulated as a Min-Max Model Predictive Control (MM-MPC) problem, where optimal accelerations are generated to minimize the cost function under the worst case, where the worst case is taken over the possible models. The proposed method is flexible in such a way that it can be applied to both homogeneous platoon and heterogeneous platoon with mixed human-driven and automated controlled vehicles. A third-order linear vehicle model with fixed feedback delay and stochastic actuator lag is used to predict the platoon behavior. Actuator lag is assumed to vary randomly with unknown distributions but a known upper bound. The controller regulates platoon accelerations over a time horizon to minimize a cost function representing driving safety, efficiency, and ride comfort, subject to speed limits, plausible acceleration range, and minimal net spacing. The designed strategy is tested by simulating homogeneous and heterogeneous platoons in a number of typical and extreme scenarios to assess the system stability and performance. The test results demonstrate that the designed control strategy for CAV can ensure the robustness of stability and performance against model uncertainties and feedback delay and outperforms the deterministic MPC based platooning control.

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A Survey on the Coordination of Connected and Automated Vehicles at Intersections and Merging at Highway On-Ramps

Cited by 686 publications

References 80 publications

Why did the robot cross the road? — Learning from multi-modal sensor data for autonomous road crossing

Why did the robot cross the road? — Learning from multi-modal sensor data for autonomous road crossing

The price of anarchy in transportation networks by estimating user cost functions from actual traffic data

A Robust Longitudinal Control Strategy of Platoons under Model Uncertainties and Time Delays

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