Control strategy for the optimization of mixed traffic flow with autonomous vehicles

Németh, Balázs; Bede, Zsuzsanna; Gáspár, Péter

doi:10.1016/j.ifacol.2019.08.075

Cited by 10 publications

(3 citation statements)

References 8 publications

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“…Chen et al [62] proposed a cellular automation model to simulate mixed traffic between AVs and HDVs. Other recent work on mixed traffic can be found in [63][64][65][66][67].…”

Section: Autonomous Vehiclesmentioning

confidence: 99%

Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

Gallo

2024

Smart Cities

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In the near future, fully autonomous vehicles may revolutionise mobility and contribute to the development of the smart city concept. In this work, we assume that vehicles are not only fully autonomous but also centrally controlled by a single operator, who can also define the traffic light control parameters at intersections. With the aim of optimising the system to achieve a global optimum, the operator can define both the routes of the fleet of vehicles and the traffic light control parameters. This paper proposes a model for the joint optimisation of traffic light control parameters and autonomous vehicle routes to achieve the system optimum. The model, which is solved using a gradient algorithm, is tested on networks of different sizes. The results obtained show the validity of the proposed approach and the advantages of centralised management of vehicles and intersection control parameters.

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“…Chen et al [62] proposed a cellular automation model to simulate mixed traffic between AVs and HDVs. Other recent work on mixed traffic can be found in [63][64][65][66][67].…”

Section: Autonomous Vehiclesmentioning

confidence: 99%

Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

Gallo

2024

Smart Cities

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“…e substantial potential to reduce the congestion would benefit not only AV drivers but also other drivers. Even though significantly increased AV users may potentially increase the congestion [13], the traffic conditions may also be improved by optimized vehicle operation and reduced crashes and delays [22,23]. With an improved transportation system, AV techniques have a significant potential to save energy and reduce emissions [17,24].…”

Section: Introductionmentioning

confidence: 99%

Safety of Autonomous Vehicles

Wang

Zhang

Huang

et al. 2020

Journal of Advanced Transportation

121

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Autonomous vehicle (AV) is regarded as the ultimate solution to future automotive engineering; however, safety still remains the key challenge for the development and commercialization of the AVs. Therefore, a comprehensive understanding of the development status of AVs and reported accidents is becoming urgent. In this article, the levels of automation are reviewed according to the role of the automated system in the autonomous driving process, which will affect the frequency of the disengagements and accidents when driving in autonomous modes. Additionally, the public on-road AV accident reports are statistically analyzed. The results show that over 3.7 million miles have been tested for AVs by various manufacturers from 2014 to 2018. The AVs are frequently taken over by drivers if they deem necessary, and the disengagement frequency varies significantly from 2 × 10−4 to 3 disengagements per mile for different manufacturers. In addition, 128 accidents in 2014–2018 are studied, and about 63% of the total accidents are caused in autonomous mode. A small fraction of the total accidents (∼6%) is directly related to the AVs, while 94% of the accidents are passively initiated by the other parties, including pedestrians, cyclists, motorcycles, and conventional vehicles. These safety risks identified during on-road testing, represented by disengagements and actual accidents, indicate that the passive accidents which are caused by other road users are the majority. The capability of AVs to alert and avoid safety risks caused by the other parties and to make safe decisions to prevent possible fatal accidents would significantly improve the safety of AVs. Practical applications. This literature review summarizes the safety-related issues for AVs by theoretical analysis of the AV systems and statistical investigation of the disengagement and accident reports for on-road testing, and the findings will help inform future research efforts for AV developments.

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“…Connected and autonomous vehicles (CAV) are thought to be a vital component for future transportation systems because of the possibility of precise control of individual vehicles and the resultant positive effects such as smoothing traffic flow, increasing throughput, and improving traffic safety (1)(2)(3)(4)(5)(6)(7). However, the driving behaviors of CAVs are likely different from those of human-driven vehicles (HV) because of their autonomy and connectivity.…”

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confidence: 99%

Multiple-Factors Aware Car-Following Model for Connected and Autonomous Vehicles

et al. 2021

Transportation Research Record: Journal of the Transportation R

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The emergence of connected and autonomous vehicles (CAV) is of great significance to the development of transportation systems. This paper proposes a multiple-factors aware car-following (MACF) model for CAVs with the consideration of multiple factors including vehicle co-optimization velocity, velocity difference of multiple PVs, and space headway of multiple PVs. The Next Generation Simulation (NGSIM) dataset and the genetic algorithm are used to calibrate the parameters of the model. The stability of the MACF model is first theoretically proved and then empirically verified via numerical simulation experiments. In addition, the VISSIM software is partially redeveloped based on the MACF model to analyze mixed traffic flows consisting of human-driven vehicles and CAVs. Results show that the integration of CAVs based on the MACF model effectively improves the average velocity and throughput of the system.

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Control strategy for the optimization of mixed traffic flow with autonomous vehicles

Cited by 10 publications

References 8 publications

Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

Safety of Autonomous Vehicles

Multiple-Factors Aware Car-Following Model for Connected and Autonomous Vehicles

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