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.

Deep Reinforcement Learning Agent with Varying Actions Strategy for Solving the Eco-Approach and Departure Problem at Signalized Intersections

2020 ◽  
Vol 2674 (8) ◽  
pp. 119-131
Author(s):  
Saleh R. Mousa ◽  
Sherif Ishak ◽  
Ragab M. Mousa ◽  
Julius Codjoe ◽  
Mohammed Elhenawy
Keyword(s):  
Reinforcement Learning ◽  
Control Problem ◽  
Fuel Consumption ◽  
Red Light ◽  
Signalized Intersections ◽  
Red Light Running ◽  
Fuel Savings ◽  
Learning Agent ◽  
Target Network ◽  
Experience Replay

Eco-approach and departure is a complex control problem wherein a driver’s actions are guided over a period of time or distance so as to optimize fuel consumption. Reinforcement learning (RL) is a machine learning paradigm that mimics human learning behavior, in which an agent attempts to solve a given control problem by interacting with the environment and developing an optimal policy. Unlike the methods implemented in previous studies for solving the eco-driving problem, RL does not require prior knowledge of the environment to be learned and processed. This paper develops a deep reinforcement learning (DRL) agent for solving the eco-approach and departure problem in the vicinity of signalized intersections for minimization of fuel consumption. The DRL algorithm utilizes a deep neural network for the RL. Novel strategies such as varying actions, prioritized experience replay, target network, and double learning were implemented to overcome the expected instabilities during the training process. The results revealed the significance of the DRL algorithm in reducing fuel consumption. Interestingly, the DRL algorithm was able to successfully learn the environment and guide vehicles through the intersection without red light running violation. On average, the DRL provided fuel savings of about 13.02% with no red light running violations.

Examining Speech-Based Auditory Alerts for Intersection Collision Warning Systems using a Driving Simulator

2018 ◽  
Vol 62 (1) ◽  
pp. 1939-1943
Author(s):  
West M. O’Brien ◽  
Xingwei Wu ◽  
Linda Ng Boyle
Keyword(s):  
Driving Simulator ◽  
Red Light ◽  
Warning System ◽  
Warning Systems ◽  
Safety Hazards ◽  
Collision Warning ◽  
Potential Safety ◽  
Auditory Alerts ◽  
Forward Collision Warning ◽  
Collision Warning Systems

Collision warning systems alert drivers of potential safety hazards. Forward collision warning (FCW) systems have been widely implemented and studied. However, intersection collision warning systems (ICWS), such as intersection movement assist (IMA), are more complex. Additional studies are needed to identify the best alert for directing the driver toward the hazard. A driving simulator study with 48 participants was conducted to examine three speech-based auditory alerts (general, directional, and command) in a simulated red light running (RLR) collision scenario. The command alert that informed the drivers to brake was the most effective in reducing the number of collisions. The post-drive questionnaire showed that drivers also rated the brake alert to be best in terms of interpretation (based on the Kruskal Wallis test). This study provides insight into the performance of different types of speech-based alerts for an intersection collision warning system and can provide guidance for future studies.

scholarly journals The Relationship between the Elapsed Time from the Onset of Red Signal until Its Violation and Traffic Accident Occurrence in Abu Dhabi, UAE

Safety ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 53
Author(s):  
Abdulla Alghafli ◽  
Effendi Mohamad ◽  
Ahmed Al Zaidy
Keyword(s):  
Traffic Accident ◽  
Red Light ◽  
Signalized Intersections ◽  
Abu Dhabi ◽  
Speed Limit ◽  
Elapsed Time ◽  
Number Of Factors ◽  
Almost All ◽  
The Relationship ◽  
Geometric Designs

Few studies have been carried out in UAE relating red light violations to a number of factors, such as speed limit violations, geometric design of the intersection, and the elapsed time from the onset of red signal until the time of the violation to the occurrence of the accident. This study bridges this gap by attempting to investigate the relationship between the elapsed time from the onset of red signal and the occurrence of the accident. To achieve this objective, Poisson’s regression, between occurrence of accident and elapsed time from the onset of red signal and the occurrence of the accident at various geometric designs of intersections (3-leg and 4-leg), was carried out. The research found that at 4-leg intersections, almost all red light violation related accidents occur between 1 to 2 s from onset of red light until its violation time. The research also showed that at 3-leg intersections, most of red light violation related accidents occur in less than 1 s from the onset of red light until violation time. It was also found that at lead lag signalized intersections, regardless of the type of the intersection, most accidents tend to occur between 2 to 3 s from the onset of red light.

scholarly journals Investigating Different Types of Red-Light Running Behaviors among Urban e-Bike Rider Mixed Groups

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fan Zhang ◽  
Chenchen Kuai ◽  
Huitao Lv ◽  
Wenhao Li
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Unobserved Heterogeneity ◽  
Red Light ◽  
Multinomial Logit Model ◽  
Time Of Day ◽  
Signalized Intersections ◽  
Traffic Light ◽  
Safety Level ◽  
Red Light Running

The red-light running (RLR) behaviors of urban mixed e-bike groups (delivery and ordinary e-bike) have become the main cause of traffic accidents at signalized intersections. The primary purpose of this study is to identify influencing factors of e-bike riders’ RLR behaviors, focusing on the role of delivery e-bike riders in mixed e-bike rider groups. Crossing behaviors of 4,180 e-bike samples (2006 delivery e-bikes and 2174 ordinary e-bikes) at signalized intersections are observed in Xi’an, China. The random parameter multinomial logit model is employed to capture the unobserved heterogeneous effects, and the effects of interaction terms are also considered. The results indicate that delivery e-bike riders are more likely to run red lights than ordinary e-bike riders. E-bike type, riders’ age, waiting position, traffic volume, traffic light type, and time of day are associated with crossing behaviors in urban mixed e-bike groups. In addition, the variable of traffic light status is found to account for unobserved heterogeneity. Findings are indicative to the development of effective implications in improving e-bikes’ traffic safety level at signalized intersections.

System Integration and Field Tests for Developing In-Vehicle Dilemma Zone Warning System

2003 ◽  
Vol 1826 (1) ◽  
pp. 53-59 ◽  
Author(s):  
Young-Jun Moon ◽  
Jooil Lee ◽  
Yukyung Park
Keyword(s):  
System Integration ◽  
Red Light ◽  
Field Tests ◽  
Warning System ◽  
Field Testing ◽  
Signalized Intersections ◽  
Dilemma Zone ◽  
Stopping Distance ◽  
Real Traffic ◽  
Warning Device

The basis for system integration and field testing was developed for assessing a dilemma zone warning system for signalized intersections. The system consists of hardware (an in-vehicle warning device, roadside antenna, and traffic signal controller) and software to operate and test the integrated component warning and communications systems. Field tests were conducted in real traffic situations to test the system’s warning initiation time from the signal controller, the activation and duration of the visual and audible signals, and the warning delay, on the basis of relationships between distance variables that include the safe stopping distance and the location of roadside antenna. Findings from the field tests at two signalized intersections indicated that the system could be implemented at signalized intersections to eliminate the dilemma zone, relative to approach speeds, and to reduce red-light violations and intersection collisions by means of an in-vehicle warning device.

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