Multimodal Trajectory Predictions for Urban Environments Using Geometric Relationships between a Vehicle and Lanes

Implementation of safe and efficient autonomous driving systems requires accurate prediction of the long-term trajectories of surrounding vehicles. High uncertainty in traffic behavior makes it difficult to predict trajectories in urban environments, which have various road geometries. To over-come this problem, we propose a method called lane-based multimodal prediction network (LAMP-Net), which can handle arbitrary shapes and numbers of traffic lanes and predict both the future trajectory along each lane and the probability of each lane being selected. A vector map is used to define the lane geometry and a novel lane feature is introduced to represent the generalized geometric relationships between the vehicle state and lanes. Our network takes this feature as the input and is trained to be versatile for arbitrarily shaped lanes. Moreover, we introduce a vehicle motion model constraint to our network. Our prediction method combined with the constraint significantly enhances prediction accuracy. We evaluate the prediction performance on two datasets which contain a wide variety of real-world traffic scenarios. Experimental results show that our proposed LAMP-Net outperforms state-of-the-art methods.