scholarly journals The Effects of the Driver’s Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5274
Author(s):  
Víctor Corcoba Magaña ◽  
Wilhelm Daniel Scherz ◽  
Ralf Seepold ◽  
Natividad Martínez Madrid ◽  
Xabiel García Pañeda ◽  
...  
Keyword(s):  
Mental State ◽  
Human Error ◽  
Traffic Accidents ◽  
Driving Simulator ◽  
Co2 Concentration ◽  
Cognitive Capacity ◽  
Strong Impact ◽  
Environment Temperature ◽  
Co2 Level ◽  
State Of The Environment

Globalization has increased the number of road trips and vehicles. The result has been an intensification of traffic accidents, which are becoming one of the most important causes of death worldwide. Traffic accidents are often due to human error, the probability of which increases when the cognitive ability of the driver decreases. Cognitive capacity is closely related to the driver’s mental state, as well as other external factors such as the CO2 concentration inside the vehicle. The objective of this work is to analyze how these elements affect driving. We have conducted an experiment with 50 drivers who have driven for 25 min using a driving simulator. These drivers completed a survey at the start and end of the experiment to obtain information about their mental state. In addition, during the test, their stress level was monitored using biometric sensors and the state of the environment (temperature, humidity and CO2 level) was recorded. The results of the experiment show that the initial level of stress and tiredness of the driver can have a strong impact on stress, driving behavior and fatigue produced by the driving test. Other elements such as sadness and the conditions of the interior of the vehicle also cause impaired driving and affect compliance with traffic regulations.

Driver Distraction Detection and Recognition

2020 ◽  
Author(s):  
Kiran Kumar Chinta ◽  
Fred Barez
Keyword(s):  
Human Error ◽  
Traffic Accidents ◽  
Driving Simulator ◽  
Complete Solution ◽  
Driver Distraction ◽  
Monitoring Systems ◽  
Head Orientation ◽  
Kinect Sensor ◽  
Behavior Monitoring ◽  
Detection And Recognition

Abstract Statistics have shown that the main reason for traffic accidents is human error. Modern vehicles are equipped to protect occupants in the event of a crash. The latest advanced vehicles come with driver behavior monitoring systems in recent years, and many have been proven to be effective systems in the prevention of accidents. However, these systems do not provide a complete solution and can only detect driver fatigue or driver distraction. This project aims to build an AI model for sensing the distraction of drivers and identifying the kind of distraction using the Kinect sensor and the Brio camera and reorient driver’s attention on driving. For this, the system is divided into three sub-segments; calling arm position (arms up or down, arms right or left), facial expressions (blinking and mouth), and head orientation. Each segment develops important info for gauging the distraction of a driver based on the depth mapping of data and color from the Kinect sensor and Brio camera respectively. Testing on a driving simulator is completed on 4 different drivers of diverse ethnicity, sex, and age along with over 240 mins of recorded material. Since all the segments were recorded and prepared separately, they can further be taken to build different outcomes and can be implemented for real car systems.

Traffic accident and emission reduction through intermittent release measures for heavy fog weather

2015 ◽  
Vol 29 (25) ◽  
pp. 1550148 ◽  
Author(s):  
Jing Shi ◽  
Jin-Hua Tan
Keyword(s):  
Traffic Safety ◽  
Emission Reduction ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Driving Simulator ◽  
Emission Characteristics ◽  
Driving Behaviors ◽  
Ca Model ◽  
Reduce Emissions

Heavy fog weather can increase traffic accidents and lead to freeway closures which result in delays. This paper aims at exploring traffic accident and emission characteristics in heavy fog, as well as freeway intermittent release measures for heavy fog weather. A driving simulator experiment is conducted for obtaining driving behaviors in heavy fog. By proposing a multi-cell cellular automaton (CA) model based on the experimental data, the role of intermittent release measures on the reduction of traffic accidents and CO emissions is studied. The results show that, affected by heavy fog, when cellular occupancy [Formula: see text], the probability of traffic accidents is much higher; and CO emissions increase significantly when [Formula: see text]. After an intermittent release measure is applied, the probability of traffic accidents and level of CO emissions become reasonable. Obviously, the measure can enhance traffic safety and reduce emissions.

scholarly journals USING MOBILE LASER SCANNING DATA FOR FEATURES EXTRACTION OF HIGH ACCURACY DRIVING MAPS

2016 ◽  
Vol XLI-B3 ◽  
pp. 433-439 ◽  
Author(s):  
Bisheng Yang ◽  
Yuan Liu ◽  
Fuxun Liang ◽  
Zhen Dong
Keyword(s):  
Laser Scanning ◽  
Human Error ◽  
Traffic Accidents ◽  
Three Dimensional ◽  
Point Clouds ◽  
High Accuracy ◽  
3D Point Clouds ◽  
Assistant Systems ◽  
Novel Method ◽  
High Flexibility

High Accuracy Driving Maps (HADMs) are the core component of Intelligent Drive Assistant Systems (IDAS), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences. Vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. This paper proposes a novel method to extract road features (e.g., road surfaces, road boundaries, road markings, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, vehicles and so on) for HADMs in highway environment. Quantitative evaluations show that the proposed algorithm attains an average precision and recall in terms of 90.6% and 91.2% in extracting road features. Results demonstrate the efficiencies and feasibilities of the proposed method for extraction of road features for HADMs.

scholarly journals Development of a Road Condition Information System using Augmented Reality Technology on Smartphones

2020 ◽  
Vol 10 (2) ◽  
pp. 49-55
Keyword(s):  
Information System ◽  
Augmented Reality ◽  
High Speed ◽  
Human Error ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Data Conversion ◽  
The Road ◽  
Road Condition ◽  
Augmented Reality Technology

Recently, accidents involving ground transportations are getting worse and more serious. Indonesian State Police (Korlantas POLRI) recorded the number of accidents in 2018 as many as 109,215 accidents. The number has incresed 4.69 percent compared to 2017 as many as 104,327 events. Road traffic accidents are caused by human error, the driver in this case. The driver's mistake is influenced by several factors, one of them is they cannot expect the road condition when they drive a vehicle at high speed. To solve this problem, drivers need information that can show road conditions. So, we present a new approach for detecting damaged roads by applying augmented reality technology. This research produces a road condition information system to help drivers get information about road conditions via smartphone. This system uses augmented reality technology with a markerless GPS Based Tracking method. The development of this system requires several stages such as collecting the data, data conversion, data classification, and views road condition. The researchers gathered the road condition data from the Public Work Department Semarang. This department itself undertakes a task to control the road condition in Semarang The trial of this system includes all drivers in Semarang city. Based on the results of the questionnaire responded to by 93 respondents, this test obtained an average value of 68%. So this system gets a pretty good response from the driver. Through this system, all drivers can avoid the damaged road condition which can cause traffic-congested and accident.

scholarly journals Using the Interval Type-2 Fuzzy Inference Systems to Compare the Impact of Speed and Space Perception on the Occurrence of Road Traffic Accidents

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1548
Author(s):  
Marjana Čubranić-Dobrodolac ◽  
Libor Švadlenka ◽  
Svetlana Čičević ◽  
Aleksandar Trifunović ◽  
Momčilo Dobrodolac
Keyword(s):  
Traffic Accidents ◽  
Driving Simulator ◽  
Fuzzy Inference ◽  
Road Traffic ◽  
Space Perception ◽  
Road Traffic Accidents ◽  
Inference System ◽  
Interval Type ◽  
The Impact

A constantly increasing number of deaths on roads forces analysts to search for models that predict the driver’s propensity for road traffic accidents (RTAs). This paper aims to examine a relationship between the speed and space assessment capabilities of drivers in terms of their association with the occurrence of RTAs. The method used for this purpose is based on the implementation of the interval Type-2 Fuzzy Inference System (T2FIS). The inputs to the first T2FIS relate to the speed assessment capabilities of drivers. These capabilities were measured in the experiment with 178 young drivers, with test speeds of 30, 50, and 70 km/h. The participants assessed the aforementioned speed values from four different observation positions in the driving simulator. On the other hand, the inputs of the second T2FIS are space assessment capabilities. The same group of drivers took two types of space assessment tests—2D and 3D. The third considered T2FIS sublimates of all previously mentioned inputs in one model. The output in all three T2FIS structures is the number of RTAs experienced by a driver. By testing three proposed T2FISs on the empirical data, the result of the research indicates that the space assessment characteristics better explain participation in RTAs compared to the speed assessment capabilities. The results obtained are further confirmed by implementing a multiple regression analysis.

scholarly journals Influence of adverse weather on drivers’ perceived risk during car following based on driving simulations

2019 ◽  
Vol 27 (4) ◽  
pp. 282-292 ◽  
Author(s):  
Chen Chen ◽  
Xiaohua Zhao ◽  
Hao Liu ◽  
Guichao Ren ◽  
Xiaoming Liu
Keyword(s):  
Perceived Risk ◽  
Traffic Accidents ◽  
Driving Simulator ◽  
Weather Conditions ◽  
Entropy Weight ◽  
Car Following ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Entropy Weight Method ◽  
Four Levels

Abstract Adverse weather has a considerable impact on the behavior of drivers, which puts vehicles and drivers in hazardous situations that can easily cause traffic accidents. This research examines how drivers’ perceived risk changes during car following under different adverse weather conditions by using driving simulation experiment. An expressway road scenario was built in a driving simulator. Eleven types of weather conditions, including clear sky, four levels of fog, four levels of rain and two levels of snow, were designed. Furthermore, to simulate the car-following behavior, three car-following situations were designed according to the motion of the lead car. Seven car-following indicators were extracted based on risk homeostasis theory. Then, the entropy weight method was used to integrate the selected indicators into an index to represent the drivers’ perceived risk. Multiple linear regression was applied to measure the influence of adverse weather conditions on perceived risk, and the coefficients were considered as indicators. The results demonstrate that both the weather conditions and road type have significant effects on car-following behavior. Drivers’ perceived risk tends to increase with the worsening weather conditions. Under conditions of extremely poor visibility, such as heavy dense fog, the measured drivers’ perceived risk is low due to the difficulties in vehicle operation and limited visibility.

Analysis of Sleep Deprivation Effect to Driving Performance Using ReactionTest Simulation

2015 ◽  
Vol 72 (4) ◽  
Author(s):  
Ika Nurlaili Isnainiyah ◽  
Febriliyan Samopa ◽  
Hatma Suryotrisongko ◽  
Edwin Riksakomara
Keyword(s):  
Sleep Deprivation ◽  
Traffic Accidents ◽  
Driving Simulator ◽  
Driving Performance ◽  
Deprivation Condition ◽  
Falling Asleep ◽  
Sleep Condition ◽  
Lack Of Sleep ◽  
The Difference ◽  
Social Habits

Sleep deprivation condition might lead to falling asleep through inappropriate situations, such as driving. Driving in a state of fatigue or drowsy from lack of sleep will be far worse than driving after alcohol consumption. Hence, the authors develop a driving simulator using Java to modify the control and rules of OpenDS application in order to simulate and calculate the automatic ReactionTest for 25 respondents simulating in both normal conditions and sleepy conditions when driving. Through this study, the authors obtained that the difference of driving performance in terms of reaction rate when driving the car in sleep deprivation condition and the normal condition, is equal to 1.08 seconds. The results also shown that the risk of loss of control that can occur to the driver of the car in units of meters (m), is equal to 0.3024 x the car’s speed. This study aims to reduce the number of traffic accidents caused by sleep deprivation that occur in society by giving a recommendation to the driver that forced to drive in lack of sleep condition. In top of that, the authors propose to create an understanding for changing the social habits of driving toward a better way.  

Sign in / Sign up

Export Citation Format

Share Document