CogSig-Mamba: Hippocampal-Inspired Explainable Motion Forecasting with Causal Temporal Attribution

Engineering notes

Evaluated on the Argoverse 2 dataset, CogSig-Mamba achieves minADE6 = 0.908 m and minFDE6 = 1.949 m with only 1.9M parameters.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Motion forecasting models in the autonomous driving domain achieve high accuracy but cannot explain their predictions, creating a barrier to safety certification. This paper presents CogSig-Mamba, a model that produces causally validated temporal explanations alongside trajectory predictions. Inspired by hippocampal memory, the model follows a five-stage process: (1) synaptic tagging, where a top-k sparse gate selects which observation windows drove the prediction; (2) evidence encoding, which consolidates window content into memory representations; (3) reverse replay, which confirms causal faithfulness of tags through removal experiments; (4) spatial context, integrating road geometry for grounded predictions; and (5) constructive retrieval, which explains each predicted behavior via mode-specific attention to produce a complete Cognitive Signature. Evaluated on the Argoverse 2 dataset, CogSig-Mamba achieves minADE6 = 0.908 m and minFDE6 = 1.949 m with only 1.9M parameters. Removing tagged windows shifts predictions by 4.6 m on average, while removing untagged windows produces a negligible impact (0.46 m), confirming causal faithfulness across all 24,988 validation scenarios. To the best of the authors’ knowledge, this is the first motion forecaster with verified temporal credit assignment, supporting the audit trails envisioned by ISO 21448 for safety-of-the-intended-functionality compliance.

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