Risk perception and the warning strategy based on safety potential field theory

Li, Linheng; Gan, Jing; Yi, Ziwei; Xu, Qing

doi:10.1016/j.aap.2020.105805

Cited by 54 publications

(15 citation statements)

References 16 publications

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“…Secondly, on single lane road, the longitudinal distance between vehicles is zero. By simplifying (14), the risk field intensity value of the following vehicle can be expressed by (26).…”

Section: ) Secondary Rear-end Collision Accident Risk Propagation Modelmentioning

confidence: 99%

“…Li thought that the risk field without acceleration parameters could not express the change of vehicle movement trend, and the circular structure could not express the difference of field intensity distribution under the influence of speed. He designed the elliptic structure risk field combined with acceleration parameters, and thus proposed the PFI traffic risk index and established the vehicle collision early warning framework [26], [27], [28]. Now, the application of risk field theory has become a powerful means of vehicle risk assessment [29].…”

Section: Introductionmentioning

confidence: 99%

“…Its assessed rear-end risk is denoted as 1/TTC . The PFI method is the potential field index method, which risk field mainly emphasizes the impact of acceleration on the field intensity [26].…”

mentioning

confidence: 99%

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A Dynamic Bayesian Network Model for Real-Time Risk Propagation of Secondary Rear-End Collision Accident Using Driving Risk Field

2022

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In order to take more active measures to prevent and control secondary accidents, it is necessary to describe risk propagation process after the accident. To this end, this paper deeply analyzed the risk propagation mechanism between vehicles and proposed a novel secondary rear-end collision accident risk propagation model, which could real-time evaluate vehicle rear-end collision risk. The research scene of the paper is a single-lane road rear-end collision scene, so a driving risk field model suitable for this scene is first established. Based on this, the vehicle operation interaction risk field force is calculated. Then, the risk field force is converted into risk probability through the hyperbolic tangent function, and the vehicle operation interaction risk model is obtained. In addition, a risk propagation framework based on dynamic Bayesian network is constructed to describe the propagation process of rear-end collision risk from the accident vehicle to following vehicles. Finally, according to the probabilistic reasoning process of the framework, combined with the accident vehicle risk, a secondary rearend collision accident risk propagation model is established. Simulation experiments show that the paper model can describe the evolution trend of rear-end risk and the risk assessment results are more accurate. And after the accident, the rear-end collision risk propagation speed will increase with the increase of traffic flow, and the number of secondary rear-end vehicles will also increase with the increase of traffic speed. These conclusions are of great significance in formulating vehicle anti-collision strategies and deploying risk management and control facilities.INDEX TERMS Secondary rear-end collision; Risk propagation; Driving risk field; Dynamic Bayesian network

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“…Secondly, on single lane road, the longitudinal distance between vehicles is zero. By simplifying (14), the risk field intensity value of the following vehicle can be expressed by (26).…”

Section: ) Secondary Rear-end Collision Accident Risk Propagation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Dynamic Bayesian Network Model for Real-Time Risk Propagation of Secondary Rear-End Collision Accident Using Driving Risk Field

2022

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“…This characterization of the driving risk degree is completely consistent with the safety risks in the actual driving process of the vehicle. In our previous studies [29,35], the vehicle potential field model was proven to accurately describe the driving risk of vehicles under different motion states in the CAVs environment.…”

Section: Platoon Formation Control Modelmentioning

confidence: 99%

A Novel Graph and Safety Potential Field Theory-Based Vehicle Platoon Formation and Optimization Method

Gan

et al. 2021

Applied Sciences

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Platooning is considered to be a very effective method for improving traffic efficiency, traffic safety and fuel economy under the connected and automated environment. The prerequisite for realizing these advantages is how to form a platoon without any collisions and how to maintain and optimize the car-following behavior after platoon formation. However, most of the existing studies focus on the platoon configuration and information transmission method, while only a few attempt to address the issue of platoon formation and optimization methods. To this end, this study proposes a novel platoon formation and optimization model combining graph theory and safety potential field (G-SPF) theory for connected and automated vehicles (CAVs) under different vehicle distributions. Compared to previous studies, we innovatively incorporate the concept of the safety potential field to better describe the actual driving risk of vehicles and ensure their absolute safety. A four-step platoon formation and optimization strategy is developed to achieve platoon preliminary formation and platoon driving optimization control. Three traffic scenarios with different CAVs distributions are designed to verify the effectiveness of our proposed platoon formation method based on G-SPF theory, and the simulation results indicate that a collision-free platoon can be formed in a short time. Additionally, the G-SPF-based platoon driving optimization control method is demonstrated by comparing it with two typical control strategies. Compared with the constant spacing and constant time headway control strategies, the simulation results show that our proposed method can improve the traffic capacity by approximately 48.8% and 26.6%, respectively.

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“…Existing studies, however, focus on the obstacle’s shape but ignore its motion states which markedly influence the collision risk of an obstacle ( 23 ). For example, a moving obstacle with a high velocity may be more dangerous than a non-moving one.…”

mentioning

confidence: 99%

Velocity Obstacle-Based Collision Avoidance and Motion Planning Framework for Connected and Automated Vehicles

Wang

et al. 2022

Transportation Research Record

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This study proposes a novel collision avoidance and motion planning framework for connected and automated vehicles based on an improved velocity obstacle (VO) method. The controller framework consists of two parts, that is, collision avoidance method and motion planning algorithm. The VO algorithm is introduced to deduce the velocity conditions of a vehicle collision. A collision risk potential field (CRPF) is constructed to modify the collision area calculated by the VO algorithm. A vehicle dynamic model is presented to predict vehicle moving states and trajectories. A model predictive control (MPC)-based motion tracking controller is employed to plan collision-avoidance path according to the collision-free principles deduced by the modified VO method. Five simulation scenarios are designed and conducted to demonstrate the control maneuver of the proposed controller framework. The results show that the constructed CRPF can accurately represent the collision risk distribution of the vehicles with different attributes and motion states. The proposed framework can effectively handle the maneuver of obstacle avoidance, lane change, and emergency response. The controller framework also presents good performance to avoid crashes under different levels of collision risk strength.

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Risk perception and the warning strategy based on safety potential field theory

Cited by 54 publications

References 16 publications

A Dynamic Bayesian Network Model for Real-Time Risk Propagation of Secondary Rear-End Collision Accident Using Driving Risk Field

A Dynamic Bayesian Network Model for Real-Time Risk Propagation of Secondary Rear-End Collision Accident Using Driving Risk Field

A Novel Graph and Safety Potential Field Theory-Based Vehicle Platoon Formation and Optimization Method

Velocity Obstacle-Based Collision Avoidance and Motion Planning Framework for Connected and Automated Vehicles

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