Ensuring public safety in the face of increasing traffic accidents is of utmost importance. LED in-ground traffic lights have been effective in reducing pedestrian accidents, but they can also lead to cognitive errors among drivers. In a recent paper published in the journal Mathematics, researchers conducted an experimental study utilizing digital twins and virtual simulators to investigate these errors.
Study: Cognitive Errors and LED Traffic Lights: A Virtual Simulation Study. Image credit: ako photography/Shutterstock
Background
The increasing use of smartphones has contributed to a rise in pedestrian accidents. LED in-ground traffic lights have proven highly effective in accident prevention. These lights synchronize with traffic signals, enhancing pedestrian visibility day and night. Installed on crosswalks and streets, they function as new pedestrian signals, discouraging jaywalking and promoting road safety. In school and senior zones, they serve as multifunctional traffic signal assistants, significantly reducing accident rates.
However, the installation of LED in-ground traffic lights poses a risk of cognitive errors, particularly misstarts and misstops by drivers who mistake them for conventional traffic lights at night. Evaluating their impact on driver cognition and their correlation with cognitive errors is crucial to understanding their causes and ensuring safety.
Construction of the experimental environment
The proposed experimental environment comprises two key components: the digital twin model and the virtual simulator, both pivotal for validating cognitive errors in our experiments.
Digital Twin Model Construction: First, researchers built a detailed 3D road model for test driving to ensure a seamless experience for test subjects. This model closely mirrored the operational rules of the traffic system, including actual traffic flow characteristics and signals. Then they created a digital twin model for a specific test course, synthesizing roads, and terrain in the SCANeR studio simulator, which is a driving simulator from AVSimulation. This includes two types of intersections: one where the driver goes straight through an intersection, utilizing a lane designated for right turns only, and another where the driver makes a right turn without the presence of such a designated lane. These intersections formed the basis of two driving courses.
After the road and course models were established, researchers integrated LED in-ground traffic lights in relevant sections positioned realistically. Finally, the model incorporated the surrounding traffic environment, including vehicles and pedestrians, based on actual traffic volume. This immersion not only influenced the test subject's driving speed but also guided them in the main scenario section. This thorough process led to the completion of the digital twin model.
Virtual Simulator Construction: The SCANeR virtual environment studio was chosen for driving simulations. The digital twin model was applied to this simulator. To get an authentic driving experience, researchers crafted a simulated cabin with features mirroring those of a real vehicle. This involved using a modified vehicle as the simulator's cabin and incorporating a motion platform and a three-channel video screen. For a seamless experience, Various navigation systems were introduced to aid in designing the virtual navigation system. It helps to design the virtual navigation system. This system guided a heads-up display location, ensuring an immersive and intuitive experience for the driver.
Experiments and analysis of cognitive behavior
To assess the cognitive errors caused by LED in-ground traffic lights, researchers conducted experiments using a virtual environment. First, they evaluated the driver based on perception, decision-making, and action by deploying several methods. Then they created three scenarios to test cognitive errors during road driving. These scenarios tested misstarts and misstops in different situations, such as when the vehicle was completely stopped, decelerating, or turning right. To conduct the experiments, researchers recruited 30 individuals with diverse ages and driving experiences. The experiment involved different driving speeds and took about two hours per person. They organized two maps with test and control groups for the driving courses, resulting in a total of 720 events. Finally, they collected data on vehicle operation, driver cognition, and cognitive errors using various devices and analyzed the data using theMATLAB and EEGLAB software.
After completing the test drives and collecting data, data preprocessing was conducted for analysis. A total of 662 cases were extracted, excluding data losses and the control group, from 720 events. Road and behavior data, driver gaze data, and bio-signal data were extracted and processed.
The time at each scenario point and vehicle signal states were extracted. Driver behaviors were analyzed to identify misrecognition patterns, particularly in scenarios one and two, involving misstarts when the LED in-ground traffic light was green. Driver's gaze data were classified based on attention to traffic lights. Hit times and gaze time rates were extracted. Bio-signal data from electroencephalogram (EEG) devices was analyzed for stress, anxiety, and signal quality. Power values and activation ratios of brainwave frequencies were recorded. Pre-validation tests confirmed that the test sequence did not significantly affect driver behavior. Validation of misstarts revealed differences in gaze patterns, stress, and adaptation, while validation of misstops revealed that LED in-ground lights had no significant effect on driver behavior.
Conclusion
In summary, researchers used a digital twin model and a virtual driving environment to assess recognition errors. The results suggested that these lights could result in misstarts, especially among drivers who tend to speed, act aggressively, or exhibit negligence. Although the lights affected starting from a complete stop, they had a lesser impact on driving behavior but attracted the driver's attention when the vehicle was stationary.