Physics-Guided Multi-Modal Motion Prediction with Interaction-Aware GRU

In the Argoverse 2 experiments reported here, the simplest Constant Turn Rate and Acceleration (CTRA) decoder was stable but missed many interaction-driven turns and merges, while residual decoders without enough control improved early displacement but increased final-horizon error. This paper therefore studies a compact decoder in which each of the six futures is represented as a CTRA anchor plus an autoregressive position residual. The residual gated recurrent unit (GRU) is initialized from fused target-history, top-k neighbor, and lane-polyline context, and its contribution is scaled by a mode-specific gate and learned exponential decay. On the 10k/2k sanity ablations, CTRA-only decoding reached (Formula presented.) m, while autoregressive residuals with a larger correction GRU reduced it to (Formula presented.) m; removing the gate increased it again to (Formula presented.) m. On the full Argoverse 2 validation split, the final configuration achieves a minimum average displacement error of (Formula presented.) m and a minimum final displacement error of (Formula presented.) m. The reported diagnostics show that the compact model generates a useful six-mode set, but still needs better probability ranking for top-1 selection.