Prediction of Near-Crashes from Observed Vehicle Kinematics using Machine Learning

Abstract

This study introduces a machine learning model for near-crash prediction from observed vehicle kinematics data. The main hypothesis is that vehicles tend to experience discernible turbulence in their kinematics shortly before involvement in near-crashes. To test this hypothesis, the SHRP2 NDS vehicle kinematics data (speed, longitudinal acceleration, lateral acceleration, yaw rate, and pedal position) are utilized. Several machine learning algorithms are trained and comparatively analyzed including K nearest neighbor (KNN), random forest, support vector machine (SVM), decision trees, Gaussian naïve Bayes (Gaussian NB), and adaptive boost (AdaBoost). Sensitivity analysis is performed to determine the optimal prediction horizon length (the time period before the occurrence of a near-crash) and the turbulence horizon length (the time period during which near-crash related changes in vehicle kinematics take place). The results indicate that optimal prediction performance can be achieved at a 1 s prediction horizons and a 3 s turbulence horizon. At these values, the AdaBoost model outperforms all other models in relation to its recall (100%), precision (98%), and F1-score (99%). These values imply that the near-crash prediction model is highly efficient in predicting most instances of near-crashes with minimal false near-crash predictions. This promising prediction performance offers a viable tool for supporting crash avoidance systems in the emerging connected/autonomous vehicle technology.

Development of a Novel Convolutional Neural Network Architecture Named RoadweatherNet for Trajectory-Level Weather Detection using SHRP2 Naturalistic Driving Data

“…The quality of trained RoadweatherNet was evaluated using the test dataset for several performance indices, including overall accuracy, precision, recall, specificity, false positive rate (FPR), and false negative rate (FNR). These indices have been widely used in the literature to evaluate the performance of machine learning models (38)(39)(40). Accuracy represents the overall ability of a model to correct classification and can be described using Equation 1.…”

Development of a Novel Framework for Hazardous Materials Placard Recognition System to Conduct Commodity Flow Studies Using Artificial Intelligence AlexNet Convolutional Neural Network

“…Precision indicates the ability of the model to classify images correctly, with no false prediction, and similarly, FDR is the opposite of precision. In general, a high degree of recall and precision of a model indicates superior performance in correct classification (63). The highest recall as well as precision was found for the no-vehicle image group, where all the test images were correctly classified.…”

Predicting Critical Bicycle-Vehicle Conflicts at Signalized Intersections

“…Other efforts have been done on predicting and classifying the upcoming near-crashes between a pair of road users in real time. Utilizing near-crash data from SHRP2 NDS datasets, Osman et al [51] applied several supervised machine learning models to predict rear-end near-crashes a few seconds before they happen. e standard deviations of Regression: goodness of fit R 2 the vehicle kinematics data such as acceleration, yaw rate, speed, and pedal position during a monitoring period before the conflicts were used as independent variables to identify the upcoming unsafe events.…”