Driver Intention and Interaction-Aware Trajectory Forecasting via Modular Multi-Task Learning
Fuad Hasan, Hailong Huang
Abstract
Forecasting the trajectories of vehicles in a highway scene is a crucial task in the area of Intelligent Transportation Systems (ITS), as the complexities of a web of driving maneuvers in a highway setting can easily lead to a catastrophe. An effective trajectory prediction thus should take into account first, what the driver wants to do (intention), as well as second, what the surrounding vehicles are going to do (interaction). This, thus, is the goal of this article. We have adopted intention awareness on two data sources and have also implemented the multi-head attention mechanism to achieve interaction awareness in order to achieve accurate future trajectory predictions. The proposed method has been trained and evaluated by following a modular multi-task learning method on two distinct publicly available datasets -NGSIM and Brain4Cars and additionally has been implemented on CARLA for evaluation of results. Experimental outcomes verify that our approach outperforms other state-of-the-art methods.