Differentiable Semantic Meta-Learning Framework for Long-Tail Motion Forecasting in Autonomous Driving

TL;DR

SAML introduces a differentiable semantic notion of tailness for long-tail motion forecasting in autonomous driving by fusing intrinsic (e.g., $C_v$, $C_ ext{j}$, $C_ ext{ω}$, $C_α$, $C_{vd}$, $C_κ$, $C_{Δκ}$, $C_{Δγ}$) and interactive ($R_{ittc}$, $R_{lon}$, $R_{lat}$, $R_{mac}$, $R_{ad}$, $R_{ni}$) properties into a continuous Tail Index (TI) via a Bayesian Tail Perceiver. TI guides a Tail-Index–driven Meta-Memory Adaptation that couples a dynamic prototype memory with a cognitive set mechanism and a MAML framework to enable rapid adaptation to rare or evolving tail patterns, augmenting multi-modal forecasts decoded by a Laplace-headed decoder. Experiments on nuScenes, NGSIM, and HighD demonstrate state-of-the-art overall accuracy and significant gains on top 1–5% worst-case events, while maintaining low latency (≈21 ms per inference). This semantic meta-learning framework offers a robust path toward safety-critical motion forecasting under distributional shift and data sparsity, with identified directions for handling extreme-tail ambiguity in future work.

Abstract

Long-tail motion forecasting is a core challenge for autonomous driving, where rare yet safety-critical events-such as abrupt maneuvers and dense multi-agent interactions-dominate real-world risk. Existing approaches struggle in these scenarios because they rely on either non-interpretable clustering or model-dependent error heuristics, providing neither a differentiable notion of "tailness" nor a mechanism for rapid adaptation. We propose SAML, a Semantic-Aware Meta-Learning framework that introduces the first differentiable definition of tailness for motion forecasting. SAML quantifies motion rarity via semantically meaningful intrinsic (kinematic, geometric, temporal) and interactive (local and global risk) properties, which are fused by a Bayesian Tail Perceiver into a continuous, uncertainty-aware Tail Index. This Tail Index drives a meta-memory adaptation module that couples a dynamic prototype memory with an MAML-based cognitive set mechanism, enabling fast adaptation to rare or evolving patterns. Experiments on nuScenes, NGSIM, and HighD show that SAML achieves state-of-the-art overall accuracy and substantial gains on top 1-5% worst-case events, while maintaining high efficiency. Our findings highlight semantic meta-learning as a pathway toward robust and safety-critical motion forecasting.

Figures (7)

• Figure 1: Conceptual comparison of (a) existing methods and (b) the proposed SAML framework. Existing methods detect long-tail events indirectly via non-interpretable clustering (a-1) or model-dependent error signals (a-2). In contrast, SAML (b) offers a principled, interpretable framework that quantifies a motion’s tailness from its intrinsic (dynamics, geometry, temporality) and interactive (local and global risk) properties, enabling robust long-tail forecasting.
• Figure 1: Visualization of multimodal motion forecasting for six right-turn scenarios (a-f) in long-tail urban environments on the nuScenes dataset. Red denotes the highest-probability forecast, and pink represents other multimodal options.
• Figure 2: Overview of the proposed SAML framework. (a) The overall model architecture. The model processes motion histories of a target agent and surrounding agents, along with HD map data, using four key modules: the Interaction-Aware Encoder, Bayesian Tail Perceiver, Meta-Memory Adaptation, and Multi-modal Decoder to generate a multimodal motion forecast. (b) and (c) Detailed illustration of the Bayesian Tail Perceiver and the Meta-Memory Adaptation modules, respectively.
• Figure 2: Visualization of multimodal motion forecasting for six left-turn scenarios (a-f) in long-tail urban environments on the nuScenes dataset. Red denotes the highest-probability forecast, and pink represents other multimodal options.
• Figure 3: Visualization of long-tail performance on the nuScenes dataset. Red denotes the highest-probability forecast, and pink represents other multimodal options.

Theorems & Definitions (10)

• Definition 1: Kinematic Dynamism
• Definition 2: Geometric Complexity
• Definition 3: Temporal Irregularity
• Definition 4: Local Interaction Risk
• Definition 5: Global Scene Risk
• Definition 1: Kinematic Dynamism
• Definition 2: Geometric Complexity
• Definition 3: Temporal Irregularity
• Definition 4: Local Interaction Risk
• Definition 5: Global Scene Risk