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In applications such as autonomous driving, it is important to understand,infer, and anticipate the intention and future behavior of pedestrians. Thisability allows vehicles to avoid collisions and improve ride safety andquality. This paper proposes a biomechanically inspired recurrent neuralnetwork (Bio-LSTM) that can predict the location and 3D articulated body poseof pedestrians in a global coordinate frame, given 3D poses and locationsestimated in prior frames with inaccuracy. The proposed network is able topredict poses and global locations for multiple pedestrians simultaneously, forpedestrians up to 45 meters from the cameras (urban intersection scale). Theoutputs of the proposed network are full-body 3D meshes represented in SkinnedMulti-Person Linear (SMPL) model parameters. The proposed approach relies on anovel objective function that incorporates the periodicity of human walking(gait), the mirror symmetry of the human body, and the change of groundreaction forces in a human gait cycle. This paper presents prediction resultson the PedX dataset, a large-scale, in-the-wild data set collected at realurban intersections with heavy pedestrian traffic. Results show that theproposed network can successfully learn the characteristics of pedestrian gaitand produce accurate and consistent 3D pose predictions.

Bio-LSTM: A Biomechanically Inspired Recurrent Neural Network for 3D Pedestrian Pose and Gait Prediction