Evaluation of a Warning System Communicating Non-Connected Red-Light-Running Vehicles to Connected Vehicles Using a Driving Simulator

2021 ◽  
Author(s):  
Hiba Nassereddine ◽  
Kelvin R. Santiago-Chaparro ◽  
Jon Riehl ◽  
David A. Noyce
Keyword(s):  
Driving Simulator ◽  
Red Light ◽  
Warning System ◽  
Connected Vehicles ◽  
Red Light Running

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 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.

Innovative countermeasures for red light running prevention at signalized intersections: A driving simulator study

2020 ◽  
Vol 134 ◽  
pp. 105349 ◽  
Author(s):  
Qinaat Hussain ◽  
Wael K.M. Alhajyaseen ◽  
Kris Brijs ◽  
Ali Pirdavani ◽  
Tom Brijs
Keyword(s):  
Driving Simulator ◽  
Red Light ◽  
Signalized Intersections ◽  
Red Light Running ◽  
Simulator Study

Auditory Messages for Intersection Movement Assist (IMA) Systems: Effects of Speech- and Nonspeech-Based Cues

2020 ◽  
pp. 001872081989197
Author(s):  
Xingwei Wu ◽  
Linda Ng Boyle
Keyword(s):  
Avoidance Behavior ◽  
Driving Simulator ◽  
Red Light ◽  
Avoidance Performance ◽  
Red Light Running ◽  
Safety Hazard ◽  
Logistic Regression Models ◽  
Warning Messages ◽  
Potential Safety ◽  
Speed Change

Objective The objective of this study was to assess the effects of different warning messages for an Intersection Movement Assist (IMA) based on drivers’ ability to avoid a potential safety hazard. Background An IMA system can detect hazards and warn drivers when it is unsafe to enter an intersection. The effects of different warning information conveyed by these systems are still unknown. Method A driving simulator study with 80 participants was conducted with a red light running (RLR) scenario using a 5 (warnings) x 2 (training) between-subject design. IMA warnings included the messages “Danger,” “Brake now,” “Vehicle on your left,” a beep, and no IMA warning. Training was provided to half of the participants. Analysis of variance and logistic regression models were used to examine differences in drivers’ avoidance behavior. Results The analyses showed that all tested warning messages can significantly enhance drivers’ avoidance performance. Significant differences were observed in crash occurrence, avoidance behavior (i.e., reaction time and speed change), and eye movements (i.e., fixation pattern and time to first fixation). The effects of training also differed given the warning message provided. Conclusion The “Brake now” message performed best in reducing crash involvement and prompted better avoidance performance. The “Danger” and “Vehicle on your left” messages improved drivers’ hazard detection ability. The training showed a potential to enhance the effectiveness of nonspeech warning messages. Application The findings of this study can help designers and engineers better design IMA warning messages for RLR scenarios.

scholarly journals Autonomous Vehicles and Vulnerable Road-Users—Important Considerations and Requirements Based on Crash Data from Two Countries

2021 ◽  
Vol 11 (7) ◽  
pp. 101
Author(s):  
Andrew Paul Morris ◽  
Narelle Haworth ◽  
Ashleigh Filtness ◽  
Daryl-Palma Asongu Nguatem ◽  
Laurie Brown ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Red Light ◽  
Road User ◽  
Red Light Running ◽  
Crash Data ◽  
Passenger Vehicles ◽  
Road Users ◽  
Vulnerable Road User ◽  
Vulnerable Road Users ◽  
Road Intersections

(1) Background: Passenger vehicles equipped with advanced driver-assistance system (ADAS) functionalities are becoming more prevalent within vehicle fleets. However, the full effects of offering such systems, which may allow for drivers to become less than 100% engaged with the task of driving, may have detrimental impacts on other road-users, particularly vulnerable road-users, for a variety of reasons. (2) Crash data were analysed in two countries (Great Britain and Australia) to examine some challenging traffic scenarios that are prevalent in both countries and represent scenarios in which future connected and autonomous vehicles may be challenged in terms of safe manoeuvring. (3) Road intersections are currently very common locations for vulnerable road-user accidents; traffic flows and road-user behaviours at intersections can be unpredictable, with many vehicles behaving inconsistently (e.g., red-light running and failure to stop or give way), and many vulnerable road-users taking unforeseen risks. (4) Conclusions: The challenges of unpredictable vulnerable road-user behaviour at intersections (including road-users violating traffic or safe-crossing signals, or taking other risks) combined with the lack of knowledge of CAV responses to intersection rules, could be problematic. This could be further compounded by changes to nonverbal communication that currently exist between road-users, which could become more challenging once CAVs become more widespread.

Real-Time Prediction of Vehicle Trajectories for Proactively Identifying Risky Driving Behaviors at High-Speed Intersections

2018 ◽  
Vol 2672 (38) ◽  
pp. 233-244 ◽  
Author(s):  
Chaopeng Tan ◽  
Nan Zhou ◽  
Fen Wang ◽  
Keshuang Tang ◽  
Yangbeibei Ji
Keyword(s):  
Phase Transition ◽  
High Speed ◽  
Red Light ◽  
Risky Behaviors ◽  
Risky Driving ◽  
Red Light Running ◽  
Driving Behaviors ◽  
Vehicle Trajectories ◽  
Single Vehicle ◽  
Risky Driving Behaviors

At high-speed intersections in many Chinese cities, a traffic-light warning sequence at the end of the green phase—three seconds of flashing green followed by three seconds of yellow—is commonly implemented. Such a long phase transition time leads to heterogeneous decision-making by approaching drivers as to whether to pass the signal or stop. Therefore, risky driving behaviors such as red-light running, abrupt stop, and aggressive pass are more likely to occur at these intersections. Proactive identification of risky behaviors can facilitate mitigation of the dilemma zone and development of on-board safety altering strategies. In this study, a real-time vehicle trajectory prediction method is proposed to help identify risky behaviors during the signal phase transition. Two cases are considered and treated differently in the proposed method: a single vehicle case and a following vehicle case. The adaptive Kalman filter (KF) model and the K-nearest neighbor model are integrated to predict vehicle trajectories. The adaptive KF model and intelligent driver model are fused to predict the following vehicles’ trajectories. The proposed models are calibrated and validated using 1,281 vehicle trajectories collected at three high-speed intersections in Shanghai. Results indicate that the root mean square error between the predicted trajectories and the actual trajectories is 5.02 m for single vehicles and 2.33 m for following vehicles. The proposed method is further applied to predict risky behaviors, including red-light running, abrupt stop, aggressive pass, speeding pass, and aggressive following. The overall prediction accuracy is 95.1% for the single vehicle case and 96.2% for the following vehicle case.

Prediction of Red Light Running Based on Statistics of Discrete Point Sensors

2009 ◽  
Vol 2128 (1) ◽  
pp. 132-142 ◽  
Author(s):  
Liping Zhang ◽  
Kun Zhou ◽  
Wei-bin Zhang ◽  
James A. Misener
Keyword(s):  
Red Light ◽  
Discrete Point ◽  
Red Light Running
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