Application of the GRAIL system for Intelligent Interaction between Automated Vehicles and Vulnerable Road Users
J. Alonso, C. Salinas, I. Parra, M. A. Sotelo
Like the Holy Grail in the medieval times, the GRAIL system provides a long-sought solution for deploying an intelligent and efficient interaction between vehicles and Vulnerable Road Users (VRUs), namely pedestrians and cyclists. GRAIL stands for GReen Assistant Interfacing Light, aiming at increasing road safety and reassuring VRUs when crossing the street. Let’s consider the situation depicted in the left hand side figure, where a couple of pedestrians are standing at the curb on a pedestrian crossing while looking for eye contact with the driver of the oncoming car. Definitely, the couple will not start crossing until they observe some signal indicating that the car is giving way to them, In order to increase VRUs reassurance, the automated system developed by the INVETT Research Group of the University of Alcalá (UAH) performs two actions in parallel that contribute to improve the interaction between the car and the VRUs. On the one hand, the vehicle starts to decrease its speed significantly as soon as those pedestrians are detected by the on-board camera system. On the other hand, the GRAIL system, an array of green diodes located in the front of the vehicle (as shown in the right hand side figure), is turned on.
Experimental set up & Demonstration
The DRIVERTIVE vehicle of the INVETT Research Group of the University of Alcalá will drive on automated mode along the proving ground while watching for pedestrians in the surrounding. At some point, a pedestrian or group of pedestrians will stand by the road side waiting to cross the street. DRIVERTIVE will detect the pedestrians’ intentions and will give way to them while switching on the GRAIL system, as an indicator of presence acknowledge. Pedestrian detections will be carried out using DRIVERTIVE onboard sensors (vision, laser and radar).
 R. Quintero, I. Parra-Alonso, D. F. Llorca, M. A. Sotelo. Pedestrian, Path, Pose, and Intention Prediction Through Gaussian Process Dynamical Models and Pedestrian Activity Recognition, IEEE Transactions on Intelligent Transportation Systems (2018). [PDF]
 R. Quintero, I. Parra, J. Lorenzo, D. Fernández-Llorca, M. A. Sotelo, Pedestrian Intention Recognition by Means of a Hidden Markov Model and Body Language, 2017 IEEE Intelligent Transportation Systems Conference. Yokohama, Japan (2017). [PDF]
 I. Parra-Alonso, R. Izquierdo, J. Alonso, A. García-Morcillo, D. F. Llorca, M. A. Sotelo. The Experience of DRIVERTIVE –DRIVERless cooperaTIVE Vehicle- Team in the 2016 GCDC, IEEE Transactions on Intelligent Transportation Systems (2018). [PDF]