SCALE-Net: Scalable Vehicle Trajectory Prediction Network under Random Number of Interacting Vehicles via Edge-enhanced Graph Convolutional Neural Network
Hyeongseok Jeon,Junwon Choi,Dongsuk Kum,Hyeongseok Jeon,Junwon Choi,Dongsuk Kum
Predicting the future trajectory of surrounding vehicles in a randomly varying traffic level is one of the most challenging problems in developing an autonomous vehicle. Since there is no pre-defined number of interacting vehicles participated in, the prediction network has to be scalable with respect to the number of vehicles in order to guarantee consistent performance in terms of both accuracy ...
