Improvement of safe stopping distance and accident risk coefficient based on active driver sight field on real road conditions

Gürbüz, Habib; Buyruk, Serhat

doi:10.1049/iet-its.2019.0322

Cited by 19 publications

(16 citation statements)

References 24 publications

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“…Based on the reaction time and tunnel's external environment influence on the sight distance of parking, Wensen 8 concluded that the reaction time is the smallest when drivers were at 40~60 m from the tunnel entrance. Bassan et al [9][10][11] studied the impact of road complexity, driving risk perception and visual stimuli on reaction time, providing a reference for the calculation of reaction distance in the parking sight distance model. Hsu 12 conducted sensitivity analyses on each variable in the parking sight distance and concluded that vehicle speed is the most sensitive factor affecting the parking sight distance.…”

Section: Literature Reviewmentioning

confidence: 99%

Drivers’ sight distance of tunnel entrance and exit based on support vector regression

Shang

Cai

et al. 2022

International Conference on Smart Transportation and City Engineering (STCE 2022)

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To study the variation law of driver's sight distance at tunnel entrance and exit, an actual vehicle test was conducted on an expressway in Chongqing (a total length of 160 km, 36 tunnels, including 4 short tunnels, 8 middle tunnels, 14 long tunnels, and 10 extra-long tunnels) in non-peak hours. Eye-tracking data, illuminance, and velocity were collected by eye-tracker, illuminance meter, and non-contact velocimetry with multi-function respectively. Based on the image illuminance extraction method, the illuminance of tunnel entrance and exit were obtained through Matlab. According to the tunnel parameters in reality, the scale model test was designed, and the driving simulation test was carried out to obtain the driver's sight distance at the tunnel entrance and exit. The support vector regression sight distance calculation model was constructed to analyze the relationship among the driver's driving sight distance, velocity, and illuminance to inspect the driver's sight distance at the entrance and exit of the tunnel and put forward some improvement measures.

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Section: Literature Reviewmentioning

confidence: 99%

Drivers’ sight distance of tunnel entrance and exit based on support vector regression

Shang

Cai

et al. 2022

International Conference on Smart Transportation and City Engineering (STCE 2022)

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“…The halo design introduces a narrower and more articulated vision through a semi-confined space, which can tax visual performance. Some critiques have also expressed concerns that the vertical bars included in the halo design may have compounding effects along with the sudden acceleration and deceleration throughout the race [2,3]. The development F1 racing car reveals that cost often restricts the battle to meet performance requirements.…”

Section: Introductionmentioning

confidence: 99%

Quantifying vision obstruction of Formula One (F1) halo concept variants

Srinivasan

Demirel

2022

Proceedings of the 7th International Digital Human Modeling Symposium -

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This paper presents an early-design methodology to quantify vision obstruction caused by halo-type cockpit safety equipment introduced into Formula One (F1) racing in 2018. The halo is a curved bar that surrounds the driver's head over the cockpit opening and offers additional protection to drivers. However, the halo's introduction has raised concerns over vision obstruction-related issues due to its vertical and horizontal bars (pillar-like elements) sitting in front of the cockpit. This study assesses vision obstructions by exploring the driver's forward field of view (FoV) based on the coverage zone analysis. This research utilizes digital manikins inserted in a digital F1 racecar mockup to assess the effects of halo concept variants on vision obstruction. The preliminary results showed that the vision obstruction was not only affected by the halo geometry and size but also the orientation of the F1 car in different racetrack segments. The methodology discussed in this study is critical for other early-stage product design and development challenges, where designers demand "quick-and-dirty" ergonomics evaluation of vision obstruction before building time-consuming and costly physical mockups.

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“…It is displayed on Google maps of the vehicle with a latency of approximately 6 s. And some scholars focus on drivers. Gürbüz and Buyruk 3 developed a model based on factors of driver, vehicle, and environment to determine the “safe stopping distance” of the vehicles. But they mainly considered the brake condition.…”

Section: Introductionmentioning

confidence: 99%

Vehicle steering control method based on machine learning

Wang

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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The environmental perception and control are the core issues of driverless vehicles. Considering the weak signal of global positioning system (GPS) and the difficulty of road information acceptance in rural area, the DIPLECS autonomous driving datasets is selected. The roads in dataset are classified by support vector machine algorithm (SVM), and established into the road template library. Then, on different roads, images and steering direction are used as the input and labels of the training set for back propagation neural network (BPNN) model. Finally, the effect of the model is verified on the video clip. The results show that the accuracy of road classification reaches 99.8% through SVM, the shortest time consumption of a single frame is 0.9 ms. The highest accuracy of steering prediction is 94.5%, and it only takes 0.224 ms to detect a frame. The algorithm meets the requirements of accuracy and real-time, and it can effectively assist and warn the drivers or automatic driving systems.

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Improvement of safe stopping distance and accident risk coefficient based on active driver sight field on real road conditions

Cited by 19 publications

References 24 publications

Drivers’ sight distance of tunnel entrance and exit based on support vector regression

Drivers’ sight distance of tunnel entrance and exit based on support vector regression

Quantifying vision obstruction of Formula One (F1) halo concept variants

Vehicle steering control method based on machine learning

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