Quantitative Indicator of Driving Risk Based on Improved Driving Safety Field

2021 ◽  
Author(s):  
Huiying Wen ◽  
Zheng Chen
Keyword(s):  
Driving Safety ◽  
Quantitative Indicator ◽  
Driving Risk

scholarly journals Effect of High-Altitude Environment on Driving Safety: A Study on Drivers’ Mental Workload, Situation Awareness, and Driving Behaviour

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xinyan Wang ◽  
Wu Bo ◽  
Weihua Yang ◽  
Suping Cui ◽  
Pengzi Chu
Keyword(s):  
High Altitude ◽  
Situation Awareness ◽  
Mental Workload ◽  
Mediating Effect ◽  
Driving Safety ◽  
Risky Driving ◽  
Driving Behaviour ◽  
Driving Risk ◽  
Low Altitude ◽  
The Relationship

This study aims to analyze the effect of high-altitude environment on drivers’ mental workload (MW), situation awareness (SA), and driving behaviour (DB), and to explore the relationship among those driving performances. Based on a survey, the data of 356 lowlanders engaging in driving activities at Tibetan Plateau (high-altitude group) and 341 lowlanders engaging in driving activities at low altitudes (low-altitude group) were compared and analyzed. The results suggest that the differences between the two groups are noteworthy. Mental workload of high-altitude group is significantly higher than that of low-altitude group, and their situation awareness is lower significantly. The possibility of risky driving behaviours for high-altitude group, especially aggressive violations, is higher. For the high-altitude group, the increase of mental workload can lead to an increase on aggressive violations, and the situation understanding plays a full mediating effect between mental workload and aggressive violations. Measures aiming at the improvement of situation awareness and the reduction of mental workload can effectively reduce the driving risk from high-altitude environment for lowlanders.

scholarly journals A Recognition Model of Driving Risk Based on Belief Rule-Base Methodology

2018 ◽  
Vol 32 (11) ◽  
pp. 1850037 ◽  
Author(s):  
Chuan Sun ◽  
Chaozhong Wu ◽  
Duanfeng Chu ◽  
Zhenji Lu ◽  
Jian Tan ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Data Driven ◽  
Driving Safety ◽  
Rule Base ◽  
Driver Assistance Systems ◽  
Recognition Model ◽  
Time Operation ◽  
Belief Rule Base ◽  
Driving Risk ◽  
Data Driven Modeling

This paper aims to recognize driving risks in individual vehicles online based on a data-driven methodology. Existing advanced driver assistance systems (ADAS) have difficulties in effectively processing multi-source heterogeneous driving data. Furthermore, parameters adopted for evaluating the driving risk are limited in these systems. The approach of data-driven modeling is investigated in this study for utilizing the accumulation of on-road driving data. A recognition model of driving risk based on belief rule-base (BRB) methodology is built, predicting driving safety as a function of driver characteristics, vehicle state and road environment conditions. The BRB model was calibrated and validated using on-road data from 30 drivers. The test results show that the recognition accuracy of our proposed model can reach about 90% in all situations with three levels (none, medium, large) of driving risks. Furthermore, the proposed simplified model, which provides real-time operation, is implemented in a vehicle driving simulator as a reference for future ADAS and belongs to research on artificial intelligence (AI) in the automotive field.

Personalized Subjective Driving Risk: Analysis and Prediction

2020 ◽  
Vol 32 (3) ◽  
pp. 503-519
Author(s):  
Naren Bao ◽  
Alexander Carballo ◽  
Chiyomi Miyajima ◽  
Eijiro Takeuchi ◽  
Kazuya Takeda ◽  
...  
Keyword(s):  
Risk Factors ◽  
Short Term Memory ◽  
Risk Model ◽  
Influential Factors ◽  
Driving Safety ◽  
Risk Level ◽  
Subjective Perception ◽  
Lane Changes ◽  
Driving Risk ◽  
Subjective Risk

Subjective risk assessment is an important technology for enhancing driving safety, because an individual adjusts his/her driving behavior according to his/her own subjective perception of risk. This study presents a novel framework for modeling personalized subjective driving risk during expressway lane changes. The objectives of this study are twofold: (i) to use ego vehicle driving signals and surrounding vehicle locations in a data-driven and explainable approach to identify the possible influential factors of subjective risk while driving and (ii) to predict the specific individual’s subjective risk level just before a lane change. We propose the personalized subjective driving risk model, a combined framework that uses a random forest-based method optimized by genetic algorithms to analyze the influential risk factors, and uses a bidirectional long short term memory to predict subjective risk. The results demonstrate that our framework can extract individual differences of subjective risk factors, and that the identification of individualized risk factors leads to better modeling of personalized subjective driving risk.

scholarly journals Driving Risk Affected Areas and Distribution Function of Sharp Horizontal Curves of Expressway

2015 ◽  
Vol 2015 ◽  
pp. 1-5
Author(s):  
Xiao-fei Wang ◽  
Xin-wei Li ◽  
Ying Yan ◽  
Xin-sha Fu
Keyword(s):  
Distribution Function ◽  
Safety Management ◽  
Distribution Patterns ◽  
Heart Rate Change ◽  
Driving Safety ◽  
Plane Curves ◽  
Horizontal Curves ◽  
Driving Risk ◽  
Vehicle Acceleration ◽  
Adjacent Line

The average death and injury intensity on sharp horizontal curves (SHCs) are much higher than those of straight sections of the expressway in China. In this paper, the statistics of crashes from 2008 to 2012 on 2200 km expressways in Guangdong province are collected, and the relationships between the radius of plane curves and the crash rate are analyzed. After that, the curved expressway section with radius equal to or less than 1000 m is defined as SHCs. According to the results of the test of the operating speed, the heart rate change of drivers, and the vehicle acceleration, the distribution patterns of driving risks on the certain SHCs were theoretically analyzed. Hence, the driving risk affected areas on adjacent line units of SHCs are determined as 200 m sections before entering or after exiting the SHCs. Combining with surveyed data, the spatial distribution of crashes on SHCs is analyzed, and the driving risk distribution function of SHCs in expressway is finally deduced. The result of this research provides a theoretical basis to enhance expressway safety management and to improve the driving safety on SHCs.

Got the Results, Now What Do You Do?

2012 ◽  
Vol 2 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Michaela Heese
Keyword(s):  
Safety Culture ◽  
Success Factors ◽  
Service Providers ◽  
Transformation Process ◽  
Driving Safety ◽  
Critical Success ◽  
Culture Transformation ◽  
Survey Results ◽  
Air Navigation Services ◽  
Navigation Service

Members of the Civil Air Navigation Services Organisation have committed themselves to measure and improve safety culture within their organizations by 2013 ( CANSO, 2010 ). This paper attempts to offer support to air navigation service providers that have already implemented a standardized safety culture survey approach, in the process of transforming their safety culture based on existing survey results. First, an overview of the state of the art with respect to safety culture is presented. Then the application of the CANSO safety culture model from theory into practice is demonstrated based on four selected case studies. Finally, a summary of practical examples for driving safety culture change is provided, and critical success factors supporting the safety culture transformation process are discussed.

Lampu Sein Helm Sepeda Berbasis Voice Recognition

2019 ◽  
Vol 11 (01) ◽  
pp. 20-25
Author(s):  
Indra Saputra ◽  
Parulian Silalahi ◽  
Bayu Cahyawan ◽  
Imam Akbar
Keyword(s):  
Voice Recognition ◽  
Driving Safety ◽  
Bicycle Helmet ◽  
Turn Signal ◽  
Voice Command ◽  
The Voice ◽  
Indicator Signal

Bicycles are not equipped with the turn signal. For driving safety, a bicycle helmet with a turn signal is designed with voice rrecognition. It is using the Arduino Nano as a controller to control the ON and OFF of turn signal lights with voice commands. This device uses a Voice Recognition sensor and microphone that placed on a bicycle helmet. When the voice command is mentioned in the microphone, the Voice Recognition sensor will detect the command specified, the sensor will automatically read and send a signal to Arduino, then the turn signal will light up as instructed, the Arduino on the helmet will send an indicator signal via the Bluetooth Module. The device is able to detect sound with a percentage of 80%. The tool can work with a distance of <2 meters with noise <71 db.

Interethnic Analysis Of Bdnf Level Quantitative Indicator In Youths

2018 ◽  
Author(s):  
N.P. Belousova
Keyword(s):  
Bdnf Level ◽  
Quantitative Indicator

Fuzzy Inference Based Obstacle Avoidance Control of Electric Powered Wheelchair Considering Driving Risk

2012 ◽  
Vol 132 (6) ◽  
pp. 952-959 ◽  
Author(s):  
Atsushi Kiso ◽  
Hiroki Murakami ◽  
Hirokazu Seki
Keyword(s):  
Obstacle Avoidance ◽  
Fuzzy Inference ◽  
Powered Wheelchair ◽  
Driving Risk ◽  
Avoidance Control

Low Reflection Automotive Display for Driving Safety

2019 ◽  
pp. 893
Author(s):  
Qian Li ◽  
Bing Zhang ◽  
Puyu Qi ◽  
Cuicui Liang ◽  
Zhiqiang Wang ◽  
...  
Keyword(s):  
Driving Safety

scholarly journals Application of Artificial Neural Networks to Streamline the Process of Adaptive Cruise Control

2021 ◽  
Vol 13 (8) ◽  
pp. 4572
Author(s):  
Jiří David ◽  
Pavel Brom ◽  
František Starý ◽  
Josef Bradáč ◽  
Vojtěch Dynybyl
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Adaptive Cruise Control ◽  
Control Unit ◽  
Driving Safety ◽  
Model Parameters ◽  
Cruise Control ◽  
Artificial Neural ◽  
Dynamic Obstacle ◽  
Braking Process

This article deals with the use of neural networks for estimation of deceleration model parameters for the adaptive cruise control unit. The article describes the basic functionality of adaptive cruise control and creates a mathematical model of braking, which is one of the basic functions of adaptive cruise control. Furthermore, an analysis of the influences acting in the braking process is performed, the most significant of which are used in the design of deceleration prediction for the adaptive cruise control unit using neural networks. Such a connection using artificial neural networks using modern sensors can be another step towards full vehicle autonomy. The advantage of this approach is the original use of neural networks, which refines the determination of the deceleration value of the vehicle in front of a static or dynamic obstacle, while including a number of influences that affect the braking process and thus increase driving safety.

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