… of motion prediction, we propose a multimodal attention Transformer encoder, which modifies
the multihead attention mechanism to multi-modal attention, and each predicted … motion …

… multi-modal motion prediction. The framework consists of three parts: recurrent neural network
to process target agent’s motion … real-world driving dataset, Waymo open motion dataset, …

… In this section, we review the existing prominent literature on perception and motion
prediction in the field of autonomous vehicles. Research works have established various method…

… To overcome this limitation, we propose an attention-based Multi-Modal Fusion Transformer
that incorporates global contextual reasoning and achieves superior driving performance. …

… in the planning and control of the autonomous driving car. … In this paper, we present a
multi-modal trajectory prediction method … to achieve multi-modal predictions with considering traffic …

… in the self-driving industry. Then, we discuss machine learning approaches for movement
prediction, … Deo and MM Trivedi, “Multi-modal trajectory prediction of surrounding vehicles with …

… autonomous vehicles, we propose a crossmodal embedding framework that demonstrates
the efficacy of the use of multiple sensor data for motion prediction… For multi-modal prediction, …

… prediction to cover all the possibilities for the future. Our method unifies intention based and
multi-modal trajectory prediction. We evaluate our method on a large realworld urban driving …

… We propose a probabilistic, multi-modal, multiobject tracking system consisting of … of motion
prediction. For objects that are hard to predict precisely, eg pedestrians, motorcycles or cars …

… predictions to train a latent DRL has not been targeted yet. To that end, we propose enhancing
urban autonomous driving using multi-modal … perception and motion prediction. These …