Balancing Safety and Traffic Throughput in Cooperative Vehicle Platooning

Smith, Stanley W.; Kim, Yeojun; Guanetti, Jacopo; Kurzhanskiy, Alexander A.; Arcak, Murat; Borrelli, Francesco

doi:10.23919/ecc.2019.8795628

Cited by 9 publications

(18 citation statements)

References 12 publications

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“…For the urban driving challenge one criterion used to judge the participating teams was throughput improvement at the traffic light. This scenario is similar to the one we considered in our previous work [15], where we focused on the trade-off between traffic throughput gains and safety.…”

Section: Introductionmentioning

confidence: 93%

“…For planning, our goal is to obtain linear, discrete time models from ( 4) and (7). We use the procedure outlined in [15] for doing so: we first linearize the leader and follower vehicle dynamics about the nominal velocities v 0 L and v 0 i , respectively, and then discretize the resulting linear models each with time step t = 0.1s, resulting in…”

Section: A Vehicle Modelsmentioning

confidence: 99%

“…We end with concluding remarks in Section VII and discuss some of the challenges we encountered during the tests, as well as potential solutions. We note that parts of Sections II -IV are adapted from our previous work [15], but the remaining content in the paper is completely new and advances platooning to an urban setting.…”

Section: Introductionmentioning

confidence: 99%

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Improving Urban Traffic Throughput With Vehicle Platooning: Theory and Experiments

Smith

Kim

Guanetti³

et al. 2020

IEEE Access

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In this paper we present a model-predictive control (MPC) based approach for vehicle platooning in an urban traffic setting. Our primary goal is to demonstrate that vehicle platooning has the potential to significantly increase throughput at intersections, which can create bottlenecks in the traffic flow. To do so, our approach relies on vehicle connectivity: vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) communication. In particular, we introduce a customized V2V message set which features a velocity forecast, i.e. a prediction on the future velocity trajectory, which enables platooning vehicles to accurately maintain short following distances, thereby increasing throughput. Furthermore, V2I communication allows platoons to react immediately to changes in the state of nearby traffic lights, e.g. when the traffic phase becomes green, enabling additional gains in traffic efficiency. We present our design of the vehicle platooning system, and then evaluate performance by estimating the potential gains in terms of throughput using our results from simulation, as well as experiments conducted with real test vehicles on a closed track. Lastly, we briefly overview our demonstration of vehicle platooning on public roadways in Arcadia, CA.INDEX TERMS Vehicle platooning, traffic throughput, model predictive control.

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Section: Introductionmentioning

confidence: 93%

Section: A Vehicle Modelsmentioning

confidence: 99%

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Improving Urban Traffic Throughput With Vehicle Platooning: Theory and Experiments

Smith

Kim

Guanetti³

et al. 2020

IEEE Access

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“…The considerable amount of literature can be found on different perspectives of platooning, such as platoon formation [ 66 , 67 ], fuel consumption [ 68 , 69 ], CO 2 emission reduction [ 70 ], communication , string stability [ 71 ], control and coordination strategies [ 72 ], and security and safety [ 73 , 74 ]. However, relatively less importance has been directed to study how to elect an experienced driver, most suitable and trustworthy candidate vehicle as a platoon leader.…”

Section: Cooperative Driving—a Comprehensive Analysis Of the Research Literaturementioning

confidence: 99%

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Malik

Khan

El-Sayed

2021

Sensors

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Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

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“…Another interesting work (Gong and Du, 2018) proposes to manage CAVs in a platooning configuration but with mixed traffic involving human drivers. In Smith et al (2019), the authors propose an approach to balance safety and traffic throughput issues in a cooperative vehicle platooning context.…”

Section: Connectivity An Automation Enablermentioning

confidence: 99%

Are Connected and Automated Vehicles the Silver Bullet for Future Transportation Challenges? Benefits and Weaknesses on Safety, Consumption, and Traffic Congestion

Gruyer

Orfila

Glaser

et al. 2021

Front. Sustain. Cities

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With the growing development of Cooperative, Connected, and Automated Mobility (CCAM), questions arise about the real impact of this innovative mobility on our daily life. CCAM originally promised to improve road safety. It is now a holistic solution for future mobility: the CCAM is there to optimize traffic, which can translate into strategies for reducing energy consumption or polluting emissions, without compromising road safety. The capability of CCAM is dependent on the reliability and robustness of its components, as it will be making life-impacting decisions. It is therefore necessary to be able to guarantee a high-level quality of sensors, communication, software, and hardware architecture. In this mobility ecosystem, the infrastructure and data that it will be able to produce is at the heart of current research issues. This paper addresses the following question: Are the Connected and automated Vehicles (CAVs) the silver bullet solution with which to answer the issues of the current mobility systems? This question is discussed by investigating the technologies used, the digital infrastructures, its robustness to cyber-attack, and their relationship with the claimed benefits on safety, energy and pollution management, traffic optimization, deployment strategy, and a link with the new generation of road infrastructures.

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Balancing Safety and Traffic Throughput in Cooperative Vehicle Platooning

Cited by 9 publications

References 12 publications

Improving Urban Traffic Throughput With Vehicle Platooning: Theory and Experiments

Improving Urban Traffic Throughput With Vehicle Platooning: Theory and Experiments

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Are Connected and Automated Vehicles the Silver Bullet for Future Transportation Challenges? Benefits and Weaknesses on Safety, Consumption, and Traffic Congestion

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