Humans have the innate ability to attend to the most relevant actors in their vicinity and can forecast how they may behave in the future. This ability will be crucial for the deployment of safety-critical agents such as robots or vehicles which interact with humans. We propose a theoretical framework for this problem setting based on autoregressively modelling sequences of nested sets, using latent variables to better capture multimodal distributions over future sets of sets. We present a new model architecture which we call a Nested Set Transformer which employs multi-head self-attention blocks over sets of sets that serve as a form of social attention between the elements of the sets at every timestep. Our approach can produce a distribution over  future trajectories for all agents under consideration , or focus upon the trajectory of an ego-agent. We validate the Nested Set Transformer for autonomous driving settings which we refer to as ("AutoBot"), where we model the trajectory of an ego-agent based on the sequential observations of key attributes of multiple agents in a scene. AutoBot produces results better than state-of-the-art published prior work on the challenging nuScenes vehicle trajectory modeling benchmark. We also examine the multi-agent prediction version of our model and jointly forecast an ego-agent's future trajectory along with the other agents in the scene. We validate the behavior of our proposed Nested Set Transformer for scene level forecasting with a pedestrian trajectory dataset .
Robust multi-agent trajectory prediction is essential for the safe control of robots and vehicles that interact with humans. Many existing methods treat social and temporal information separately and therefore fall short of modelling the joint future trajectories of all agents in a socially consistent way. To address this, we propose a new class of Latent Variable Sequential Set Transformers which autoregressively model multi-agent trajectories. We refer to these architectures as "AutoBots". AutoBots model the contents of sets (e.g. representing the properties of agents in a scene) over time and employ multi-head self-attention blocks over these sequences of sets to encode the sociotemporal relationships between the different actors of a scene. This produces either the trajectory of one ego-agent or a distribution over the future trajectories for all agents under consideration . Our approach works for general sequences of sets and we provide illustrative experiments modelling the sequential structure of the multiple strokes that make up symbols in the Omniglot data. For the single-agent prediction case, we validate our model on the NuScenes motion prediction task and achieve competitive results on the global leaderboard. In the multi-agent forecasting setting, we validate our model on TrajNet. We find that our method outperforms physical extrapolation and recurrent network baselines and generates scene-consistent trajectories .
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Humans have the innate ability to attend to the most relevant actors in their vicinity and can forecast how they may behave in the future. This ability will be crucial for the deployment of safety-critical agents such as robots or vehicles which
Robust multi-agent trajectory prediction is essential for the safe control of robots and vehicles that
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We propose a theoretical framework for this problem setting based on autoregressively modelling sequences of nested sets, using latent variables to better capture multimodal distributions over future sets of sets. We present a new model architecture which we call a Nested Set Transformer which employs
Many existing methods treat social and temporal information separately and therefore fall short of modelling the joint future trajectories of all agents in a socially consistent way. To address this, we propose a new class of Latent Variable Sequential Set Transformers which autoregressively model multi-agent trajectories. We refer to these architectures as "AutoBots". AutoBots model the contents of sets (e.g. representing the properties of agents in a scene) over time and employ
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sets of sets that serve as a form of social attention between the elements of the sets at every timestep. Our approach can produce
these sequences of sets to encode the sociotemporal relationships between the different actors of a scene. This produces either the trajectory of one ego-agent or
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the
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, or focus upon the trajectory of an ego-agent. We validate the Nested Set Transformer for autonomous driving settings which we refer to as ("AutoBot"), where we model the trajectory of an ego-agent based on the sequential observations of key attributes of multiple agents in a scene. AutoBot produces results better than state-of-the-art published prior work on the challenging nuScenes vehicle trajectory modeling benchmark. We also examine the multi-agent prediction version of our model and jointly forecast an ego-agent's future trajectory along with the other agents in the scene. We validate the behavior of our proposed Nested Set Transformer for scene level forecasting with a pedestrian trajectory dataset
. Our approach works for general sequences of sets and we provide illustrative experiments modelling the sequential structure of the multiple strokes that make up symbols in the Omniglot data. For the single-agent prediction case, we validate our model on the NuScenes motion prediction task and achieve competitive results on the global leaderboard. In the multi-agent forecasting setting, we validate our model on TrajNet. We find that our method outperforms physical extrapolation and recurrent network baselines and generates scene-consistent trajectories