Evaluation of a collision prediction system for VRUs using V2X and machine learning: intersection collision avoidance for motorcycles

Abstract

The safety factor of ITS is particularly important for VRUs, as they are typically more prone to accidents and fatalities than other road users. The implementation of safety systems for these users is challenging, especially due to their agility and hard to predict intentions. Still, using ML mechanisms on data that is collected from V2X communications, has the potential to implement such systems in an intelligent and automatic way. This paper evaluates the performance of a collision prediction system for VRUs (motorcycles in intersections), by using LSTMs on V2X data-generated using the VEINS simulation framework. Results show that the proposed system is able to prevent at least 74% of the collisions of Scenario A and 69% of Scenario B on the worst case of perception-reaction times; In the best cases, the system is able to prevent 94% of the collisions of Scenario A and 96% of Scenario B.