scholarly journals Effect of Warning System on Interactive Driving Behavior at Unsignalized Intersection under Fog Conditions: A Study Based on Multiuser Driving Simulation

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yunfan Zhang ◽  
Xuedong Yan ◽  
Jiawei Wu ◽  
Ke Duan
Keyword(s):  
Collision Avoidance ◽  
Warning System ◽  
Driving Behavior ◽  
Driving Simulation ◽  
Driving Safety ◽  
Collision Warning ◽  
Action Point ◽  
Increased Risk ◽  
Simulation Scenario ◽  
Further Development

The intersection collision warning system (ICWS) is an emerging technology designed to assist drivers in avoiding collisions at intersections. ICWS has an excellent performance in reducing the number of collisions and improving driving safety. Previous studies demonstrated that when visibility was low under fog conditions, ICWS could help drivers timely detect hazardous conflicting vehicles. However, the influences of ICWS on interactive driving behavior at unsignalized intersection between different vehicles have barely been discussed. This study aimed to investigate the patterns of drivers’ interactive behaviors with the assistance of a new kind of ICWS under fog conditions based on Multiuser Driving Simulation. The Multiuser Driving Simulation allowed multiple drivers to operate in the same simulation scenario at the same time, and it could capture drivers’ interactions preferably. Forty-eight licensed drivers completed the driving simulation experiment in three fog conditions (no fog, light fog, and heavy fog) and two warning conditions (warning and no warning), in which the drivers drove in a straight-moving situation at unsignalized intersection with potential collision risks caused by the encounter of two vehicles. The results verified that warning and fog conditions were significant factors that affected the interactive driving behavior in the unsignalized intersection collision avoidance process, including the driver’s decision, TTC of action point, average acceleration (deceleration) rate, and postencroachment time. Compared to conditions without ICWS, the ICWS could help drivers make collision avoidance actions earlier and change the speed more smoothly. In addition, with the help of Multiuser Driving Simulation, associations between decision driving behaviors of vehicles were discussed with caution. The results revealed the decision-making mechanism of drivers in the process of interaction with other drivers. Under the influence of fog, interactive driving processes were fraught with increased risk at unsignalized intersection. However, the ICWS helped drivers interact more harmoniously, safely, and efficiently. The findings shed some light on the further development of ICWS and the study on interactive driving behavior.

scholarly journals Fuzzy-Based Adaptive Cruise Controller with Collision Avoidance and Warning System

2013 ◽  
Vol 3 (1) ◽  
pp. 143 ◽  
Author(s):  
Wafa Batayneh ◽  
Omar Al-Araidah ◽  
Khaled Bataineh ◽  
Adnan Al-Ghasem
Keyword(s):  
Control System ◽  
Collision Avoidance ◽  
Adaptive Cruise Control ◽  
Warning System ◽  
Cruise Control ◽  
Collision Warning ◽  
Adaptive Cruise Control System ◽  
Cruise Control System ◽  
Control Scheme ◽  
Simulation Results

The paper presents a Fuzzy-based adaptive cruise control system with collision avoidance and collision warning (ACC/CA/CW). The proposed control scheme aims to improve driver's comfort while keeping him/her safe by avoiding possible collisions. Depending on inputs from both the driver and the installed sensors, the controller accelerates/decelerates the vehicle to keep its speed at the desired limit. In case of a possible collision, the controller decelerates (accelerates) the vehicle to prevent possible crash with the vehicle ahead (behind). Moreover, the controller issues visual and/or audio alerts for the driver in order to warn him/her in case of the need for applying an uncomfortable deceleration level and/or to warn the driver for risky situations where he/she might need to change the lane. Simulation results illustrate the robustness of the proposed system over various ranges of inputs.

Normal and Hard Braking Behavior at Stop Signs and Traffic Signals

2005 ◽  
Vol 49 (22) ◽  
pp. 1897-1901 ◽  
Author(s):  
Suzanne E. Lee ◽  
Sarah B. Brown ◽  
Miguel A. Perez ◽  
Zachary R. Doerzaph ◽  
Vicki L. Neale
Keyword(s):  
Collision Avoidance ◽  
Warning System ◽  
Traffic Signals ◽  
Performance Tests ◽  
Algorithm Development ◽  
Intersection Problem ◽  
Collision Warning ◽  
And Performance ◽  
Forward Collision Warning

A testbed intersection violation warning system was developed to address the problem of intersection crashes. The effectiveness of such systems is fundamentally dependent on the driver-braking model used to decide if a warning should be issued to the driver. If the model is unrealistic, drivers can either be annoyed due to assumed braking levels that are too low, or can be warned too late if braking expectations are too high. Initial algorithm development relied on data from the Collision Avoidance Metrics Partnership (CAMP) Forward Collision Warning (FCW) project. However, it was unknown whether the CAMP data (collected in the presence of stopped lead vehicles) would be applicable to the intersection problem (e.g., will drivers respond similarly to red traffic signals and stopped lead vehicles). Braking profile and performance tests were thus conducted to determine the applicability of the CAMP FCW results to the intersection violation warning.

scholarly journals Discrimination of Effects between Directional and Nondirectional Information of Auditory Warning on Driving Behavior

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Yuting Zhang ◽  
Xuedong Yan ◽  
Zhuo Yang
Keyword(s):  
Reference Group ◽  
Red Light ◽  
Warning System ◽  
Driving Behavior ◽  
Signalized Intersections ◽  
Directional Information ◽  
Red Light Running ◽  
Collision Warning ◽  
The Relationship ◽  
Auditory Warning

This study examines the impacts of directional and nondirectional auditory warning information in a collision warning system (CWS) on driving behavior. The data on driving behavior is collected through experiment, with scenarios containing unexpected hazard events that include different warning content. As drivers approached the collision event, either a CWS auditory warning was given or no warning was given for a reference group. Discriminant analysis was used to investigate the relationship between directional auditory warning information and driving behavior. In the experiment, the CWS warnings significantly reduced brake reaction time and prompted drivers to press the brake pedal more heavily, demonstrating the effectiveness of CWS warnings in alerting drivers to avoid red-light running (RLR) vehicles when approaching a signalized intersection. Providing a clear warning with directional information about an urgent hazard event could give drivers adequate time to prepare for the potential collision. In terms of deceleration, a directional information warning was shown to greatly help drivers react to critical events at signalized intersections with more moderate braking. From these results, requirements can be derived for the design of effective warning strategies for critical intersections.

Improved Position and Attitude Determination Method for Monocular Vision in Vehicle Collision Warning System

2016 ◽  
Vol 30 (07) ◽  
pp. 1655019 ◽  
Author(s):  
Lijuan Qin ◽  
Ting Wang ◽  
Yulan Hu ◽  
Chen Yao
Keyword(s):  
Real Time ◽  
Solution Method ◽  
Warning System ◽  
Time Characteristic ◽  
Monocular Vision ◽  
Theoretical Research ◽  
Driving Safety ◽  
License Plate ◽  
Collision Warning ◽  
Vehicle Collision

Vehicle collision warning system can determine the relative distance and speed between target vehicle and the front vehicle by monocular vision positioning technique from automobile license plate image captured by camera so as to judge danger level and remind the driver to make appropriate action and avoid vehicle collision timely. Study on the positioning technology of this system aims to help the driver to judge and improve driving safety. Thus, the system has a broad application prospect. The research content of this paper could enrich and supply PNL visual locating method, endowing with significance of theoretical research. The paper proposes an improved vehicle measuring method based on monocular vision for vehicle license plate. This method combines the characteristics of fast speed for analytical solution method and high positioning accuracy for iterative solution method, therefore has a high robustness and overcomes the multi-solution problem of P3P iterative method. The simulation experiments show that localization precision of the improved positioning method has been improved greatly as compared with P4L method. At the same time, the real-time characteristic of collision avoidance warning system with improved visual locating method has been improved a lot, and the new location algorithm has good performance in real-time characteristic, which greatly improve the processing ability of the system for images.

Human trust model of collision warning through pupillary and electromyography responses

2019 ◽  
Author(s):  
◽  
Xiaonan Yang
Keyword(s):  
Linear Regression ◽  
Collision Avoidance ◽  
Regression Models ◽  
Phase 1 ◽  
Physiological Responses ◽  
Warning System ◽  
Linear Regression Models ◽  
Fuzzy Linear Regression ◽  
Collision Warning ◽  
Trust In Automation

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] This pioneering work on human trust in automation was modeled by two main physiological measurements responses to collision avoidance warning as observed by pupil and electromyography (EMG) signals long regarded as meaningful physiological responses to danger. As an advanced driver assistance system (ADAS) becomes popular, distraction-related crashes caused by frequent false warnings make drivers' trust in ADAS is likely to deteriorate. In particular, trust is one of the most important driver cognitive characteristics that can determine the willingness to rely on and use the ADAS. Hence, it is important to investigate the driver's trust changes related to the collision warning. Previous research was limited to a single physiological response, or survey responses, and ocused on measurements of simple physical reaction instead of on human trust in automation. Accordingly, the driver's trust in a collision avoidance warning system under complex driving circumstances was not well studied. This study extended and enhanced past studies to multiple physiological responses to explore driver trust in collision warning and the role trust plays in avoidance of potential hazards and vulnerability. The purpose of this research was to assess drivers' dynamic learned trust of a collision avoidance warning system through physiological responses. In this multi-phase study, the Tobii eye-tracking device and Myo armbands were used to collect pupillary and EMG responses. During phase 1 study, aftermarket ADAS devices were used to collect drivers' natural responses to the collision warnings under open road real driving. A significant pattern changing of pupil EMG data only exits when drivers responded to warning. The findings of phase 1 demonstrated that pupillary and electromyography responses could be used together as effective indicators when drivers received valuable information and chose to make a physical response to the warning. The study noted that drivers often responded only to a warning in which they identified a potential hazard in situations characterized by uncertainty and vulnerability. As the lab offering an opportunity for simulated danger while studies in natural environments occur under conditions that are largely safe, the phase 2 study was designed as laboratory-based with under controlled environmental factors, to reveal the underlying pupillary and electromyography responses under potential hazards. For the model development, the time series features of pupil dilation and EMG data were extracted as independent variables, while the frustration based trust level was set as a dependent variable. Fuzzy linear regression models were built as quantitative measures of drivers' trust in the collision warning by using pupillary and EMG data. Classification rates of different fuzzy linear regression models were compared to the traditional linear regression model in both development and validation scenarios. Results indicate that the prediction models of drivers' trust, is improved upon by this study's possibility linear regression method (PLR) with waveform length time-series feature of pupil and EMG data as inputs, to more effectively predict drivers' trust in their collision warning system. New understanding of human dynamic learned trust in collision warning systems may provide benefits by improving driving safety and the usability of ADAS. Results from this study could contribute to future software algorithm development in a next-generation smart vehicle that can identify not only potential surrounding hazards, but also drivers' trust status, in order to provide a safer driving experience. Additionally, the findings of this study are anticipated to lead to the improvement of collision warning system development to enhance safety and improved device-user interaction.

Robustness Analysis of License Plate Visual Positioning Method in Vehicle Collision Warning System

2017 ◽  
Vol 31 (01) ◽  
pp. 1750001 ◽  
Author(s):  
Lijuan Qin ◽  
Ting Wang
Keyword(s):  
Robustness Analysis ◽  
Warning System ◽  
Driving Safety ◽  
License Plate ◽  
Positioning Accuracy ◽  
Robust Analysis ◽  
Collision Warning ◽  
Location Method ◽  
Visual Positioning ◽  
Positioning Method

Vehicle anti-collision warning system is a key research area of vehicle safety. It is also a necessary means to enhance driving safety and reduce traffic accidents. The visual location method is presented for this system based on license plate image. This method realizes visual positioning with the help of the mounting points of license plate. At the same time, the feasible robust analysis method is proposed for the visual positioning method with license plate frame image. Finally, the robust analysis determines good angles for commutating rotation parameter in the method of license plate image visual positioning. At the same time, it determines good angles for translation parameter and for rotation and translation transform. Therefore, robust analysis determines positions where the visual positioning method has high positioning accuracy. Robust analysis for license plate vision positioning method is useful in analysis of high positioning accuracy positions for the proposed license plate image visual location method combined with plate mounting points. The research content of this paper is beneficial to driver decision-making and improves the safety of collision warning system and intelligent vehicle.

Benefit Evaluation of Crash Avoidance Systems

1998 ◽  
Vol 1621 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Datta N. Godbole ◽  
Raja Sengupta ◽  
James Misener ◽  
Natasha Kourjanskaia ◽  
James B. Michael
Keyword(s):  
Collision Avoidance ◽  
Large Scale ◽  
Warning System ◽  
Operating Conditions ◽  
Real World Data ◽  
Collision Warning ◽  
General Terms ◽  
Crash Avoidance ◽  
Forward Collision Warning ◽  
Hierarchical Assessment

A five-layer hierarchy to integrate models, data, and tools is proposed for benefits assessment and requirements development for crash avoidance systems. The framework is known as HARTCAS: Hierarchical Assessment and Requirements Tools for Crash Avoidance Systems. The analysis problem is multifaceted and large-scale. The driving environment is diverse and uncertain, driver behavior and performance are not uniform, and the range of applicable collision avoidance technologies is wide. Considerable real-world data are becoming available on certain aspects of this environment, although the collection of experimental data on other aspects is constrained by technological and institutional issues. Therefore, analyses of collision avoidance systems are to be conducted by collecting data on nominal operating conditions to the greatest extent possible and by using such data to build models for analysis of the rare, abnormal conditions. HARTCAS provides a framework within which to structure the collection and use of such knowledge. It is described in general terms, and its use is illustrated by analysis of a forward collision warning system. How to quantify the relationships between the effectiveness of a warning and the probability that the warning is a nuisance is shown. System benefits are also quantified.

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