Prediction of the Future State of Pedestrians While Jaywalking Under Non-Lane-Based Heterogeneous Traffic Conditions

This study proposes a novel framework to predict jaywalkers' future state in non-lane-based heterogeneous traffic conditions by combining the effects of the surrounding dynamics with jaywalkers' poses. Different variables, such as the pedestrian pose, walking speed, location in the road environment, count and direction of approaching traffic, speed and type of closest approaching vehicle, and so forth, are used as input variables. The dataset for this study consists of 47,588 samples gathered by analyzing 1753 jaywalkers under non-lane-based heterogeneous traffic situations. Keypoint detection on the pedestrian body is made using MediaPipe. YOLOv4 and DeepSORT are used to detect and track road users to get trajectory data. Training and testing datasets are prepared for different prediction horizons to test the proposed models’ applicability for roads of varying design speeds. Four machine learning models based on ensemble techniques, namely random forest (RF), adaptive boosting (AdaBoost), gradient boosting, and extreme gradient boosting, are trained and tested for different prediction horizons from 0.5 to 4 s. Up to the prediction horizon of 1 s, all models performed equally well with Area under the ROC curve (AUC) values above 0.95. At higher prediction horizons, the RF is found to outperform the other models. All models, except AdaBoost, maintained an AUC value of greater than 0.9 when predicting future states up to a maximum of 2.5 s. The proposed model performs well for both short-term and long-term predictions by combining the effect of surrounding dynamics with pedestrian stance and speed. The outcomes can be utilized to assist infrastructure-to-vehicle connectivity in empowering vehicles to navigate through jaywalkers safely, enhancing pedestrian safety.