Early ramp warning using vehicle behavior analysis

Cui, Hua; Wei, Zefa; Wang, Xuan; Song, Xinxin; Li, Pannong; Song, Huansheng; Ma, Xiang

doi:10.1007/s00500-017-2827-9

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…Mahmud et al [10,11] analyzed the efects of dynamic message signs at diferent positions on curves with radius and investigated the efects of dynamic message sign borders, display text size, and roadside placement distance on the warning system. Gates et al [12] studied the infuence of diferent placement distances and display content on various vehicle types while Cui et al [13] classifed the collision risk on-ramps based on the analysis of vehicle trajectories and collision risk and guided drivers to pass through the ramps in an orderly manner by issuing information on the message board. In addition, researchers have devised ramp warning methods based on cooperative vehicle infrastructure system (CVIS).…”

Section: Introductionmentioning

confidence: 99%

Assessing the Effects of Interchange Warning Systems on Driving Risk: A Driving Simulator Study

Li,

Guo,

et al. 2024

Journal of Advanced Transportation

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To investigate the effects of proactive safety control systems suitable for highway interchanges and improve road traffic safety. Simulated driving experiments were conducted to test the effects of the interchange warning system (IWS) on the ramp, merging section, diverging section, and accident section. Random forest (RF) and SHapley Additive exPlanations (SHAP) are used to analyze the effects between driving behavior and driving risk change in both situations without and with IWS. The results show that (1) as driving risk increases, drivers tend to increase the frequency of braking and engage in more comprehensive saccade behaviors. Concurrently, there is an increase in acceleration and speed variation, leading to a gradual decrease in speed. (2) Compared with the SVR and XGBoost, RF can better fit the nonlinear relationship between driving risk and driver behavior characteristics with the application of IWS. (3) The IWS mainly reduces driving risk by affecting operation behavior. When the mean speed, speed standard deviation (SD), acceleration SD, and maximum braking depth are at 40 to 70 km/h, 3 to 10 km/h, 0 to 0.6 m/s2, and 14 to 16, respectively, there is a significant reduction in driving risk. The application of the IWS expands the effective range of mean speed and speed SD for reducing driving risk to 40 to 100 km/h and 3 to 15 km/h, respectively.

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Section: Introductionmentioning

confidence: 99%

Assessing the Effects of Interchange Warning Systems on Driving Risk: A Driving Simulator Study

Li,

Guo,

et al. 2024

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Vehicle behaviour analysis is a higher level of semantic interpretation of the road environment with an aim to improve traffic efficiency and reduce traffic accidents by enhancing the ability of vehicles or monitoring equipment to sense the road environment. Thus, it is extremely important to improve existing intelligent transportation systems with vehicle behaviour analysis technology [1]. Vision‐based vehicle behaviour analysis is in its nascent stages, especially compared to vehicle detection and tracking, and an ongoing challenge for intelligent transportation research.…”

Section: Introductionmentioning

confidence: 99%