Predicting the behavior of road users in rural areas for self-driving cars

Introduction. The prediction module generates possible future trajectories of dynamic objects that enables a self-driving vehicle to move safely on public roads. However, all modern prediction methods evaluate their performance only under urban conditions and do not consider their applicability to the domain of rural roads. This work examined the adaptability of existing methods to work under rural unstructured conditions and suggested a new, improved approach.Materials and Methods. As a solution, we propose to use a neural network that includes the following submodules: a graph-based scene encoder, a multimodal trajectory decoder, and a trajectory filtering module. Another proposed feature is to use an adapted loss function that penalizes the network for generating trajectories that go beyond the drivable area. These elements use standard practices for solving the prediction problem and adapting it to the domain of rural roads.Results. The presented analysis described the basic features of the prediction module in the rural road domain, showed a comparison of popular models, and discussed its applicability to new conditions. The paper describes the new approach that is more adaptive to the considered domain of study. A simulation of the new domain was performed by modifying existing public datasets.Discussion and Conclusion. Comparison to other popular methods has shown that the proposed approach provides more accurate results. The disadvantages of the proposed approach were also identified and possible solutions were described.