X‐Gen: Synergizing Extended LSTM and Generative Diffusion for Cognition‐Aware Motion Forecasting.
Saved in:
| Title: | X‐Gen: Synergizing Extended LSTM and Generative Diffusion for Cognition‐Aware Motion Forecasting. |
|---|---|
| Authors: | Liu, Liu1 (AUTHOR) liuliu@cueb.edu.cn, Xu, Zhifei2 (AUTHOR), Murray, Richard (AUTHOR) rmurray@wiley.com |
| Source: | International Journal of Intelligent Systems. 6/3/2026, Vol. 2026, p1-14. 14p. |
| Subjects: | Long short-term memory, Probabilistic generative models, Knowledge representation (Information theory), Machine learning, Autonomous vehicles |
| Abstract: | Motion forecasting in autonomous driving has historically relied on discriminative baselines that prioritize statistical correlation over causal reasoning, often failing to capture the complex social dynamics of real‐world traffic. To bridge this cognitive gap, we introduce X‐Gen, a novel generative framework that synergizes the inferential depth of foundation models with the probabilistic precision of diffusion models. Unlike prior approaches constrained by heuristic textual prompts, X‐Gen establishes a direct semantic alignment between continuous kinematic states and the high‐dimensional embedding space of a pretrained Llama‐3‐8B backbone via quantized low‐rank adaptation (Q‐LoRA). This architecture allows the system to internalize scene semantics as a coherent "World Model." Furthermore, to resolve long‐range topological dependencies, we propose a lane‐aware cognitive gating mechanism powered by the extended LSTM (xLSTM). Leveraging xLSTM's matrix memory and exponential gating, this module selectively filters spatial constraints with linear computational complexity, effectively pruning the search space for navigational intent. Finally, we eschew restrictive parametric distributions in favor of a conditional diffusion decoder, which formulates trajectory synthesis as an iterative denoising process guided by the aligned cognitive features. Empirical validation on the large‐scale Waymo open motion dataset demonstrates that X‐Gen establishes new state‐of‐the‐art benchmarks in both accuracy and diversity metrics. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Motion forecasting in autonomous driving has historically relied on discriminative baselines that prioritize statistical correlation over causal reasoning, often failing to capture the complex social dynamics of real‐world traffic. To bridge this cognitive gap, we introduce X‐Gen, a novel generative framework that synergizes the inferential depth of foundation models with the probabilistic precision of diffusion models. Unlike prior approaches constrained by heuristic textual prompts, X‐Gen establishes a direct semantic alignment between continuous kinematic states and the high‐dimensional embedding space of a pretrained Llama‐3‐8B backbone via quantized low‐rank adaptation (Q‐LoRA). This architecture allows the system to internalize scene semantics as a coherent "World Model." Furthermore, to resolve long‐range topological dependencies, we propose a lane‐aware cognitive gating mechanism powered by the extended LSTM (xLSTM). Leveraging xLSTM's matrix memory and exponential gating, this module selectively filters spatial constraints with linear computational complexity, effectively pruning the search space for navigational intent. Finally, we eschew restrictive parametric distributions in favor of a conditional diffusion decoder, which formulates trajectory synthesis as an iterative denoising process guided by the aligned cognitive features. Empirical validation on the large‐scale Waymo open motion dataset demonstrates that X‐Gen establishes new state‐of‐the‐art benchmarks in both accuracy and diversity metrics. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 08848173 |
| DOI: | 10.1155/int/3301987 |
