Intelligent network vehicle driving risk field modeling and path planning for autonomous obstacle avoidance

Luo, Jiufei; Li, Sijun; Li, Haiqing; Xia, Fuhao

doi:10.1177/09544062221085886

Cited by 12 publications

(5 citation statements)

References 38 publications

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“…For PFC simulation, the target path is defined as a known volume, which can be referred to as Li [35]. DLC maneuvers are employed to validate the proposed PFC.…”

Section: Pfc With Different DCmentioning

confidence: 99%

Personalized Collision Avoidance Control for Intelligent Vehicles Based on Driving Characteristics

Gao

Cai

et al. 2023

WEVJ

Self Cite

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Collision avoidance has been widely researched in the field of intelligent vehicles (IV). However, the majority of research neglects the individual driver differences. This paper introduced a novel personalized collision avoidance control (PCAC) strategy for IV based on driving characteristics (DC), which can better satisfy various scenarios and improve drivers’ acceptance. First, the driver’s DC is initially classified into four types using K-means clustering, followed by the application of the analytic hierarchy process (AHP) method to construct the DC identification model for the PCAC design. Then, a novel PCAC is integrated with a preview-follower control (PFC) module, an active rear steering (ARS) module, and a forward collision control (FCC) module to ensure individual requirements and driving stability. Moreover, simulations verified the validity of the developed PCAC in terms of path tracking, lateral acceleration, and yaw rate. The research results indicate that DC can be identified effectively through APH, and PCAC based on DC can facilitate the development of intelligent driving vehicles with superior human acceptance performance.

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“…For PFC simulation, the target path is defined as a known volume, which can be referred to as Li [35]. DLC maneuvers are employed to validate the proposed PFC.…”

Section: Pfc With Different DCmentioning

confidence: 99%

Personalized Collision Avoidance Control for Intelligent Vehicles Based on Driving Characteristics

Gao

Cai

et al. 2023

WEVJ

Self Cite

View full text Add to dashboard Cite

show abstract

“…By modeling a driving risk field, one can assess environmental risks and gather relevant variable information about the current environmental conditions. Through real-time monitoring and analysis of driving risks, this model allows for the assessment of real-time risks [17][18][19][20][21][22][23][24][25] in driving environments and the planning of feasible paths [21]. It also facilitates the prediction of potential risks under different road and traffic conditions.…”

Section: Introductionmentioning

confidence: 99%

Driving Style Recognition Method Based on Risk Field and Masked Learning Techniques

Jin,

Zhu,

Liu

et al. 2024

Mathematics

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With the increasing demand for road traffic safety assessment, global concerns about road safety have been rising. This is particularly evident with the widespread adoption of V2X (Vehicle-to-Everything) technology, where people are more intensively focused on how to leverage advanced technological means to effectively address challenges in traffic safety. Through the research of driving style recognition technology, accurate assessment of driving behavior and the provision of personalized safety prompts and warnings have become crucial for preventing traffic accidents. This paper proposes a risk field construction technique based on environmental data collected by in-vehicle sensors. This paper introduces a driving style recognition algorithm utilizing risk field visualization and mask learning technologies. The research results indicate that, compared to traditional classical models, the improved algorithm performs excellently in terms of accuracy, stability, and robustness, enhancing the accuracy of driving style recognition and enabling a more effective evaluation of road safety.

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“…As a typical application of intelligent networked vehicle technology, intelligent networked vehicle formation driving realizes the function of vehicles driving stably with smaller spacing, which greatly improves road accessibility and effectively reduces the consumption of onboard energy, which will be an indispensable part of the future intelligent transportation system [5][6]. However, smaller workshop distance means higher traveling risk; how to guarantee the driving safety of an intelligent networked fleet is the core problem of current intelligent networked fleet control technology [7][8].…”

Section: Introductionmentioning

confidence: 99%

Formation Mechanism and Analysis of Intelligent Connected Vehicles Fleet under Meta-Cellular Automata Simulation Conditions

Zhao,

Wen,

Wang

et al. 2024

Applied Mathematics and Nonlinear Sciences

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This paper analyzes the intelligent networked fleet formation mechanism under adaptive cruise control (CACC). By constructing a communication architecture system including network layer, coordination layer, control layer and physical layer, and based on environment sensing technology and 5G network, a layered control structure for autonomous driving vehicles is established. In the study, the inter-vehicle distance control strategy is proposed, and combined with the theory of metacellular automata, the study constructs an intelligent networked fleet model, which adopts the full speed difference model and adaptive cruise control vehicle following model. The simulation analysis is carried out through the actual data of a section of G6 Beijing-Tibet Expressway, and the simulation environment is a two-lane highway of 3000m, considering different CAV vehicle penetration rates (0% to 100%). The results show that with the increase of CAV penetration, the traffic flow and the congestion significantly improved. The intelligent Connected Vehicles fleet performs well in the obstacle avoidance task, effectively reducing unnecessary lane changes and shortening the congestion time compared to the conventional system, with the distance error controlled within 1m. This study shows that the intelligent connected vehicles-connected fleet has significant advantages in improving traffic flow’s operational efficiency and safety.

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Intelligent network vehicle driving risk field modeling and path planning for autonomous obstacle avoidance

Cited by 12 publications

References 38 publications

Personalized Collision Avoidance Control for Intelligent Vehicles Based on Driving Characteristics

Personalized Collision Avoidance Control for Intelligent Vehicles Based on Driving Characteristics

Driving Style Recognition Method Based on Risk Field and Masked Learning Techniques

Formation Mechanism and Analysis of Intelligent Connected Vehicles Fleet under Meta-Cellular Automata Simulation Conditions

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