Improving car-following model to capture unobserved driver heterogeneity and following distance features in fog condition

Huang, Yan; Yan, Xuedong; Li, Xiaomeng; Duan, Ke; Rakotonirainy, Andry; Gao, Zhijun

doi:10.1080/23249935.2022.2048917

Cited by 10 publications

(6 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The intelligent driver model (IDM) is a classical model in the field of car-following models, and it is still frequently improved to adapt to various car-following scenarios [ 38 , 39 , 40 ]. Huang et al developed the time variability of the time headway in the IDM model [ 38 ], and Jiang et al proposed stochastic factors to change the unique relationship between speed and distance headway [ 39 ]. In this study, based on the formulation of the IDM and introducing the above characteristics, an e-bicycle model was proposed.…”

Section: Methodsmentioning

confidence: 99%

Numerical Analysis of an Autonomous Emergency Braking System for Rear-End Collisions of Electric Bicycles

Zhao,

Li,

Huang

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

The rapid growth in the number of electric bicycles (e-bicycles) has greatly improved daily commuting for residents, but it has also increased traffic collisions involving e-bicycles. This study aims to develop an autonomous emergency braking (AEB) system for e-bicycles to reduce rear-end collisions. A framework for the AEB system composed of the risk recognition function and collision avoidance function was designed, and an e-bicycle following model was established. Then, numerical simulations were conducted in multiple scenarios to evaluate the effectiveness of the AEB system under different riding conditions. The results showed that the probability and severity of rear-end collisions involving e-bicycles significantly decreased with the application of the AEB system, and the number of rear-end collisions resulted in a 68.0% reduction. To more effectively prevent rear-end collisions, a low control delay (delay time) and suitable risk judgment criteria (TTC threshold) for the AEB system were required. The study findings suggested that when a delay time was less than or equal to 0.1 s and the TTC threshold was set at 2 s, rear-end collisions could be more effectively prevented while minimizing false alarms in the AEB system. Additionally, as the deceleration rate increased from 1.5 m/s2 to 4.5 m/s2, the probability and average severity of rear-end collisions also increased by 196.5% and 42.9%, respectively. This study can provide theoretical implications for the design of the AEB system for e-bicycles. The established e-bicycle following model serves as a reference for the microscopic simulation of e-bicycles.

show abstract

Section: Methodsmentioning

confidence: 99%

Numerical Analysis of an Autonomous Emergency Braking System for Rear-End Collisions of Electric Bicycles

Zhao,

Li,

Huang

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Building on this framework, Tan et al [15] subsequently integrated the "velocity imitation" phenomenon by incorporating a function that accounts for the velocities of multiple vehicles in the neighboring lane and the focal vehicle. Huang [16] took into account driver heterogeneity in foggy conditions and improved the Intelligent IDM model to accurately depict drivers' car-following characteristics. Soria et al [17] evaluated four car-following models under different traffic and weather conditions and for various driver types using field data.…”

Section: Introductionmentioning

confidence: 99%

Calibration of freeway car-following models under rain-fog environments

Mu,

Ran

et al. 2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

View full text Add to dashboard Cite

“…Car following is one of the most common situations that drivers encounter on the roads [ 15 ]. It is of great importance to investigate the safety issues relating to the takeover in the situation of car following.…”

Section: Introductionmentioning

confidence: 99%

Examining the Effects of Visibility and Time Headway on the Takeover Risk during Conditionally Automated Driving

Peng

Chen

2022

IJERPH

View full text Add to dashboard Cite

The objective of this study is to examine the effects of visibility and time headway on the takeover performance in L3 automated driving. Both non-critical and critical driving scenarios were considered by changing the acceleration value of the leading vehicle. A driving simulator experiment with 18 driving scenarios was conducted and 30 participants complete the experiment. Based on the data obtained from the experiment, the takeover reaction time, takeover control time, and takeover responses were analyzed. The minimum Time-To-Collision (Min TTC) was used to measure the takeover risk level and a binary logit model for takeover risk levels was estimated. The results indicate that the visibility distance (VD) has no significant effects on the takeover control time, while the time headway (THW) and the acceleration of the leading vehicle (ALV) could affect the takeover control time significantly; most of the participants would push the gas pedal to accelerate the ego vehicle as the takeover response under non-critical scenarios, while braking was the dominant takeover response for participants in critical driving scenarios; decreasing the TCT and taking the appropriate takeover response would reduce the takeover risk significantly, so it is suggested that the automation system should provide the driver with the urgency of the situation ahead and the tips for takeover responses by audio prompts or the head-up display. This study is expected to facilitate the overall understanding of the effects of visibility and time headway on the takeover performance in conditionally automated driving.

show abstract

Improving car-following model to capture unobserved driver heterogeneity and following distance features in fog condition

Cited by 10 publications

References 58 publications

Numerical Analysis of an Autonomous Emergency Braking System for Rear-End Collisions of Electric Bicycles

Numerical Analysis of an Autonomous Emergency Braking System for Rear-End Collisions of Electric Bicycles

Calibration of freeway car-following models under rain-fog environments

Examining the Effects of Visibility and Time Headway on the Takeover Risk during Conditionally Automated Driving

Contact Info

Product

Resources

About