Dynamic Driving Risk Potential Field Model Under the Connected and Automated Vehicles Environment and Its Application in Car-Following Modeling

Li, Linheng; Gan, Jing; Ji, Xinkai; Xu, Qing

doi:10.1109/tits.2020.3008284

Cited by 76 publications

(26 citation statements)

References 35 publications

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“…• Car-following model based on the safety potential field The microscopic traffic model that will be used as the basis for describing the car-following behavior, which is called the driving risk potential field model (DRPFM) [1]. The reason why this model was adopted in this paper lies in the fact that it has the ability to describe the dynamic distribution of the driving risks.…”

Section: A Modeling Frameworkmentioning

confidence: 99%

“…Through the analysis of our previous studies, it can be concluded that the safety potential field model dynamically describes the motion state of each traffic subject in a complex traffic environment [1]. The subject vehicle can determine its microscopic motion behavior according to the distribution of the total potential field of its surrounding traffic environment.…”

Section: A Modeling Frameworkmentioning

confidence: 99%

“…According to our previous research, the Artificial Bee Colony Algorithm (ABC) is used for the specific parameter calibration process. The results of the parameter calibration are shown in Table 1 [1].…”

Section: Figure 2 the Distribution Of Safety Potential Field With DImentioning

confidence: 99%

“…Most importantly, the vehicles can perceive some movement information that human drivers cannot directly perceive, such as acceleration and the steering angle of surrounding vehicles. Therefore, the vehicles can provide feedback on the required motion behavior in combination with the actual traffic environment and driving requirements [1][2]. Benefit from those characteristics described above, CAVs have the potential for the improvement of traffic flow performance in many aspects (e.g.,traffic safety, traffic efficiency, stability performance, and energy-saving) [3]- [8].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Macroscopic Model of Heterogeneous Traffic Flow Based on the Safety Potential Field Theory

Gan

et al. 2021

IEEE Access

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Section: A Modeling Frameworkmentioning

confidence: 99%

Section: A Modeling Frameworkmentioning

confidence: 99%

Section: Figure 2 the Distribution Of Safety Potential Field With DImentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Macroscopic Model of Heterogeneous Traffic Flow Based on the Safety Potential Field Theory

Gan

et al. 2021

IEEE Access

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“…The layout of road signs and markings and the design of the road geometry will greatly affect the actual operation of vehicles because the impact of the traffic environment on vehicle travel is difficult to quantify with a specific indicator. In recent years, a novel research method for driving safety has been proposed [27][28][29]. In our previous study, we proposed a potential field model to better describe the driving behavior of vehicles under the CAVs environment [30].…”

Section: √ √ √mentioning

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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Safe and Human‐Like Trajectory Planning of Self‐Driving Cars: A Constraint Imitative Method

Cui,

Hu,

et al. 2023

Advanced Intelligent Systems

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Safe and human‐like trajectory planning is crucial for self‐driving cars. While model‐based planning has demonstrated reliability, it is beneficial to incorporate human demonstrations and align the results with human behaviors. This work aims at bridging the gap between model‐based planning and driver imitation by proposing a constraint imitative trajectory planning method (CITP). CITP integrates artificial potential field and dynamic movement primitives, which have achieved both the ability to imitate human demonstrations as well as ensure safety constraints. During the planning process, CITP first encodes human demonstrations, local driving target, and traffic obstacles as attractive or repulsive effects, and then the trajectory planning problem is solved through model predictive optimization. To address the dynamics of traffic scenarios, a hierarchical planning strategy is proposed based on the division of planning process. CITP is designed with five modules, including LSTM‐based target generation, encoding attractive and repulsive effects with target, demonstrations and obstacles, and trajectory planning with model predictive optimization. Data collection and experiments are carried out based on CARLA driving simulator, and the effectiveness in terms of both safety and consistency with human behavior are reported.

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Dynamic Driving Risk Potential Field Model Under the Connected and Automated Vehicles Environment and Its Application in Car-Following Modeling

Cited by 76 publications

References 35 publications

A Macroscopic Model of Heterogeneous Traffic Flow Based on the Safety Potential Field Theory

A Macroscopic Model of Heterogeneous Traffic Flow Based on the Safety Potential Field Theory

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

Safe and Human‐Like Trajectory Planning of Self‐Driving Cars: A Constraint Imitative Method

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