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

Ma, Huaqing; Wu, Hao; Hu, Yucong; Chen, Zhiwei; Luo, Jieting

doi:10.1177/03611981211045205

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…Autonomous vehicles choose the appropriate driving strategy based on the scenario they are in, depending on which a multi-factor perceptional following model is proposed. This considers several factors such as vehicle synergistic optimization, velocity difference of multiple CAV vehicles, and spacing of multiple HDVs, through which the average velocity and traffic flow of the system can be effectively improved ( 4 ). Chen et al extracted information from the strategy process of human drivers and developed a lane-changing algorithm for autonomous vehicles.…”

Section: Review On Driving Decision-makingmentioning

confidence: 99%

“…At present, there is rich experience in both domestic and international research in relation to MTF. Researchers have proposed driving strategy methods using dynamical models ( 4 ), mathematical algorithms ( 5 ), and game theory ( 6 ). With the development of onboard sensors and network connectivity technology, CAVs collect data through onboard sensors as the input to strategy methods and subsequently analyze the data to obtain a CAV-appropriate driving strategy ( 7 ).…”

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confidence: 99%

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Deep Q-Network-Based Efficient Driving Strategy for Mixed Traffic Flow with Connected and Autonomous Vehicles on Urban Expressways

Wang

Zhao

et al. 2023

Transportation Research Record: Journal of the Transportation R

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With the increase in the number of automated vehicles, roads will contain a mix of automated vehicles and human-driven vehicles. At present, rule-based driving strategy control of automated vehicles in mixed traffic flow makes it difficult to obtain optimal control. Therefore, this study proposes a learning-based driving strategy for connected and autonomous vehicles under mixed traffic flow. The proposed method differs from other driving strategies in two respects. First, both the lane-change and car-following policies are included, and the Deep Q-network algorithm is utilized to train the two policies in a mixed traffic-flow environment. Second, the proposed driving strategy considers both traffic efficiency and safety when designing the reward function. Through simulation experiments, the differences in traffic efficiency and safety of this method and the rule-based method were compared and analyzed under different traffic densities and penetration rates of connected and autonomous vehicles. Simulation results show that the driving strategy improves the average velocity (by 7.02 km/h) and traffic safety (especially in high-density traffic), compared with traditional rule-based driving strategy.

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Section: Review On Driving Decision-makingmentioning

confidence: 99%

mentioning

confidence: 99%

Deep Q-Network-Based Efficient Driving Strategy for Mixed Traffic Flow with Connected and Autonomous Vehicles on Urban Expressways

Wang

Zhao

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…Despite the existence of numerous AS pilot programs globally, there is a notable absence of comprehensive and robust guidelines to ensure the effectiveness of these programs in achieving reliable outcomes and valuable perceptions [7]. The swift advancements in CAV technology, coupled with the superior performance of managed lanes, have attracted growing interest in exploring the potential benefits of CAVs operating within such lanes [14]. Hence, the primary goal of this study is to address the gaps in the literature by providing a novel approach for examining the effects of introducing AS on dedicated lanes through the intuitively designed scenarios under microscopic environment.…”

Section: Introductionmentioning

confidence: 99%

Urban Mobility Dynamics in the Realm of Autonomous Shuttles on Dedicated Lanes: A Microscopic Synthesis

Desta,

Tóth

2023

IEEE Access

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The growing interest in utilizing autonomous vehicles (AVs) for public transportation is increasingly evident in urban areas, and numerous locations worldwide have set up demonstration sites to test autonomous shuttles (ASs). However, there is a significant necessity to investigate the future traffic operational performance resulting from such services, in addition to the user behavior-based studies conducted during pilot projects. In this paper, an experimental approach is presented using a microsimulation environment to develop the traffic model with dedicated AS lanes. The approach examines different combinations of conventional vehicles (CVs), ASs, AV driving logics, and geometric modifications required to accommodate the AS service. The traffic performance of the network is evaluated using five measures of effectiveness (MoEs) at various market penetration rates (MPRs). The results reveal that the cautious driving behavior poses challenges primarily during the early stages of the MPR, and the same holds true at very high MPRs or if this driving behavior is sustained for an extended period. Thus, it is advisable to transition towards adopting higher driving logics (Normal and Aggressive), which exhibit highly promising results in meeting expectations. The trend of the results clearly indicates that utilizing dedicated lanes for AS buses is a potential option for integrating the emerging mobility solution into the current transportation system. The findings of this study are of significant importance, as they offer valuable quantitative insights that enable key stakeholders to make well-informed decisions on AS deployment.

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“…Their model has made significant progress in mitigating traffic fluctuations and reducing fuel consumption and car emissions. Ma et al ( 3 ) proposed a multi-factor perceived following model of connected autonomous vehicles (CAVs), which considers multiple factors such as vehicle collaborative optimization speed, speed difference, and spatial headway. In mixed traffic flow composed of human-driven vehicles (HVs) and CAVs, it can effectively improve the average speed and traffic capacity of the system.…”

mentioning

confidence: 99%

Research on Congestion Mitigation of Coordinated Control for Connected Vehicles on Pick/Drop Stand Road

Liu

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Pick/drop stand road (PDSR) is necessary for specific functions, but it very easily generates congestion and thus results in a less safe and comfortable ride for passengers. The Internet of Vehicles has offered new ideas and technical means to solve the problem. First, this study divides the PDSR into different functional zones and constructs the coordinated control environment of connected vehicles (CVs). A car-following coordinated control strategy (CF-CCS) and a lane-changing coordinated control strategy (LC-CCS) are proposed based on a mixed platoon of CVs and human-driven vehicles. For CF-CCS, this study offers a model for car-following coordinated platoon control based on terminal state prediction of the leading vehicle, with platoon safety and passengers’ comfort as optimization objectives, and solves it using a modified particle swarm algorithm. For LC-CCS, a model for lane-changing coordinated control of platoon is proposed, and a numerical approach is utilized to solve it. It is based on finding the best area for lane-changing location while considering the safety of lane changing and the efficiency of vehicle traffic on the PDSR. Last, VISSIM and MATLAB simulations are conducted to prove that the proposed two strategies can effectively ease congestion, significantly increase the speed of traffic flow on PDSR, and provide passengers with safer and more comfortable rides.

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

Cited by 7 publications

References 30 publications

Deep Q-Network-Based Efficient Driving Strategy for Mixed Traffic Flow with Connected and Autonomous Vehicles on Urban Expressways

Deep Q-Network-Based Efficient Driving Strategy for Mixed Traffic Flow with Connected and Autonomous Vehicles on Urban Expressways

Urban Mobility Dynamics in the Realm of Autonomous Shuttles on Dedicated Lanes: A Microscopic Synthesis

Research on Congestion Mitigation of Coordinated Control for Connected Vehicles on Pick/Drop Stand Road

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