Machine Learning-based Trajectory Prediction for VRU Collision Avoidance in V2X Environments
Raúl Parada, Anton Aguilar, Jesús Alonso-Zárate, Francisco Vázquez-Gallego
Abstract
The fifth generation (5G) of communication networks aims to accelerate the adoption of incipient vertical industries which will leverage innovative smart applications and services such as Cooperative, Connected and Automated Mobility. One of the objectives globally within that area is reducing to zero the number of fatal vehicle accidents. Unfortunately, human errors are the main cause of them, where vulnerable road users (VRUs) are involved in half of the cases. A possible approach to reduce accidents is estimating the probability of collision between two vehicles based on their estimated trajectories. These trajectories are usually tracked on-board using sophisticated devices such as cameras and LiDAR. However, VRUs are generally not equipped with such equipment and, ideally, VRUs carry smartphones with active geolocation capabilities based on satellite-based positioning systems. In this paper, we propose a novel vehicular service based on a regression algorithm to predict trajectories by uniquely using Cartesian coordinates. We compare different types of regression techniques in terms of prediction time window, position accuracy and processing time using Weka. Results show that the Alternating Model Tree (AMT) technique can predict the next position with an error of less than 3.2 centimeters, increasing up to 1 meter when predicting the next 5 positions with a period of 1 second between consecutive positions. In this case, a prediction time window of 5 s is processed within 1.25 milliseconds. AMT resulted as the lowest complex and most accurate algorithm in a multiple-step prediction position.