An improved car-following model considering variable safety headway distance

Jia, Yunfeng; Du, Yiman; Wu, Jianping

doi:10.4006/0836-1398-27.4.616

Cited by 8 publications

(5 citation statements)

References 16 publications

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“…The second type is the study of the safe distance logic algorithm [10]- [12]. The safety distance is the minimum distance at which a vehicle can prevent collision with obstacles under the current driving conditions.…”

Section: B Brief Review Of the Existing Safety Warning Researchmentioning

confidence: 99%

A Study on an Anthropomorphic Car-Following Strategy Framework of the Autonomous Coach in Mixed Traffic Flow

et al. 2020

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In this study, to explore the demand characteristics of autonomous coaches in mixed traffic flow, two sections of an expressway were selected for vehicle experiments. With personification as the goal, sensors of the ego-vehicle were used to collect naturalistic car-following behavior data of surrounding coaches. After analyzing the naturalistic car-following data, car-driving behavior characteristics of the coach drivers were acquired. The analysis results indicate that the overall car-following processes of coaches tend to be relatively stable, and most of the car-following processes are in the conditions of higher velocity, smaller acceleration, and smaller relative velocity than the front vehicle. The expected following spacing and time headway of the coach drivers increase with the increase of velocity. By analysis and comparison with existing safe following models, it is found that while the safety and the warning rate based on the car-following coach safety distance model are higher, the warning rate of the car-following time headway model at the relatively higher velocity is lower. The coach safety distance model established in this study considers the car-following risk and the acceptance rate of the drivers to the warning system. This model conforms to the driver's car-following characteristics and fulfills the requirements of collision avoidance. Applying the safety distance model to the autonomous coach would effectively improve the anthropopathic behavior characteristics of the autonomous coach. Additionally, the abnormality caused by the interference of other vehicles in mixed traffic flow of autonomous and human vehicles is successfully avoided under the premise of ensuring the safe driving of the autonomous coach. INDEX TERMS Autonomous coach, car-following safety, anthropomorphic, warning model.

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Section: B Brief Review Of the Existing Safety Warning Researchmentioning

confidence: 99%

A Study on an Anthropomorphic Car-Following Strategy Framework of the Autonomous Coach in Mixed Traffic Flow

et al. 2020

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“…How to accurately measure complex road traffic environments and reasonably plan vehicle movement trajectory is of great significance for building a safe, efficient, and intelligent transportation network [1] [2] 。 There are many existing trajectory planning methods, as shown in Table 1, which lists the advantages and disadvantages of some of the current main trajectory planning algorithms. However, when applied to vehicle trajectory planning, more methods are based on vehicle kinematics and dynamics theory, and the expression of vehicle driving safety mainly relies on the vehicle's speed, acceleration, yaw rate, and relative motion state of the two vehicles [3] [4] . These methods are difficult to reflect the impact of various traffic factors on driving safety, and it is difficult to reflect the interaction and dynamic changes of driver behavior characteristics, traffic environment, and vehicle status.…”

Section: Introductionmentioning

confidence: 99%

Extension and Application of Driving Risk Field Model Considering Vehicle Motion Constraints

Sun,

Hu,

Zhang

et al. 2023

IEEE Access

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Autonomous vehicle trajectory planning method based on the driving risk field is one of the current research hotspots. Currently, mainstream driving risk field models (DRFM) rarely consider basic factors such as vehicle geometry and heading angle, and ignore the impact of driving behavior characteristics on the driving risk field, the accuracy of safety assessment needs to be improved. Therefore, this paper proposes an extended driving risk field model (EDRFM) that considers geometric dimensions, heading angle, and driving behavior characteristics, and provides the basis for determining geometric dimensions and shape parameters, expanding the applicability of the DRFM. On this basis, EDRFM is applied to vehicle trajectory planning, and it is found that the target trajectory (denoted as P1) obtained using EDRFM is not achievable and the vehicle is unstable. This paper further proposes an arc correction method considering vehicle dynamics constraints to correct P1 to get P2. To ensure that the trajectory planning results meet the driving stability and motion of the vehicle. The results show that the EDRFM model considering arc correction can effectively complete vehicle trajectory planning tasks in complex scenes, and ensure the feasibility of trajectory planning and vehicle driving stability.INDEX TERMS Autonomous driving, trajectory planning, driving risk field, arc correction, vehicle dynamics.

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“…Many researchers have conducted thorough studies on collision avoidance planning in ICVs. [9][10][11][12][13] Common trajectory planning algorithms include random search algorithm, 14 particular function, 15 artificial potential field (APF), 16,17 and model prediction. 18 Besides these methods, Julien et al 19 used Bessel curve optimization methods to cope with the problem of reactivity and adaptability to obstacle crossing in path planning methods.…”

Section: Introductionmentioning

confidence: 99%

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

Luo

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Autonomous obstacle avoidance and decision-making algorithms for intelligent connected vehicles in a complicated transportation environment are important studies on intelligent driving. However, it is difficult to adapt to a more complicated traffic environment based on safety distance and conventional potential field. Therefore, in this paper, a driving risk field model based on field theory is proposed involving transportation factors and vehicle conditions. A hidden Markov model was used to evaluate and determine the motion state of surrounding vehicles. A safe, feasible, and smooth collision-free path was planned by calculating the magnitude of the potential field forces on the longitudinal and lateral sides of the obstacle vehicles. The results showed that the method can effectively select a suitable path for obstacle avoidance in complex road conditions while satisfying safety and traffic laws.

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An improved car-following model considering variable safety headway distance

Cited by 8 publications

References 16 publications

A Study on an Anthropomorphic Car-Following Strategy Framework of the Autonomous Coach in Mixed Traffic Flow

A Study on an Anthropomorphic Car-Following Strategy Framework of the Autonomous Coach in Mixed Traffic Flow

Extension and Application of Driving Risk Field Model Considering Vehicle Motion Constraints

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

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