Investigating Personalized Driving Behaviors in Dilemma Zones: Analysis and Prediction of Stop-or-Go Decisions

Dilemma zones at signalized intersections present a commonly occurring yet unsolved challenge in traffic safety. The onsets of yellow-light prompts varied responses from drivers: some may brake abruptly, compromising ride comfort, while others may accelerate, increasing the likelihood of red-light violations and potential safety hazards. This heterogeneity in drivers' stop-or-go (SoG) decisions stems from the surrounding traffic conditions, vehicle states, and individual driver characteristics. Consequently, identifying personalized driving behaviors and integrating them into advanced driver assistance systems (ADAS) to mitigate the dilemma zone issues poses a compelling scientific question. However, there are no open-source datasets specifically designed for studying driving behaviors (especially personalized behaviors) in dilemma zones. To fill these gaps, this study leverages a game engine-based (i.e., CARLA-enabled) driving simulator to collect high-resolution vehicle trajectories, traffic signal phase and timing information, and SoG decisions from twenty drivers across various dilemma zone scenarios. The dataset is publicly available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GitHub</uri> , facilitating in-depth analysis and modeling of personalized driving behaviors in dilemma zones. Furthermore, we propose a personalized convolutional neural network (CNN)-based model by incorporating the personalized information, including distance to stop-line, average speed, and average acceleration at the moment of SoG decision-making, and the probability of choosing the “Go” decision, to predict SoG decisions and decision times. The proposed model improves the accuracy of the SoG decision prediction by 5.9% and reduces the mean squared error (MSE) of decision time predictions by 18.8% compared to the Baseline CNN model without differentiating individual driving behaviors.