MPCFormer explicitly models multi-vehicle social interaction dynamics via physics-informed discrete state-space and Transformer-learned coefficients, yielding 0.86m ADE over 5s and 94.67% planning success with near-zero collisions in closed-loop tests.
TransFuser: Imitation With Transformer -Based Sensor Fusion for Autonomous Driving
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NTR adds a self-distillation masked latent reconstruction objective that uses only scene tokens to reconstruct masked patch features, improving visual representation quality and planning performance in end-to-end autonomous driving.
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MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
MPCFormer explicitly models multi-vehicle social interaction dynamics via physics-informed discrete state-space and Transformer-learned coefficients, yielding 0.86m ADE over 5s and 94.67% planning success with near-zero collisions in closed-loop tests.
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NTR: Neural Token Reconstruction for Scene Token Bottleneck in End-to-End Driving
NTR adds a self-distillation masked latent reconstruction objective that uses only scene tokens to reconstruct masked patch features, improving visual representation quality and planning performance in end-to-end autonomous driving.