Academic

K-Gen: A Multimodal Language-Conditioned Approach for Interpretable Keypoint-Guided Trajectory Generation

arXiv:2603.04868v1 Announce Type: new Abstract: Generating realistic and diverse trajectories is a critical challenge in autonomous driving simulation. While Large Language Models (LLMs) show promise, existing methods often rely on structured data like vectorized maps, which fail to capture the rich, unstructured visual context of a scene. To address this, we propose K-Gen, an interpretable keypoint-guided multimodal framework that leverages Multimodal Large Language Models (MLLMs) to unify rasterized BEV map inputs with textual scene descriptions. Instead of directly predicting full trajectories, K-Gen generates interpretable keypoints along with reasoning that reflects agent intentions, which are subsequently refined into accurate trajectories by a refinement module. To further enhance keypoint generation, we apply T-DAPO, a trajectory-aware reinforcement fine-tuning algorithm. Experiments on WOMD and nuPlan demonstrate that K-Gen outperforms existing baselines, highlighting the eff

M
Mingxuan Mu, Guo Yang, Lei Chen, Ping Wu, Jianxun Cui · · 1 min read · 2 views
#cs.AI

arXiv:2603.04868v1 Announce Type: new Abstract: Generating realistic and diverse trajectories is a critical challenge in autonomous driving simulation. While Large Language Models (LLMs) show promise, existing methods often rely on structured data like vectorized maps, which fail to capture the rich, unstructured visual context of a scene. To address this, we propose K-Gen, an interpretable keypoint-guided multimodal framework that leverages Multimodal Large Language Models (MLLMs) to unify rasterized BEV map inputs with textual scene descriptions. Instead of directly predicting full trajectories, K-Gen generates interpretable keypoints along with reasoning that reflects agent intentions, which are subsequently refined into accurate trajectories by a refinement module. To further enhance keypoint generation, we apply T-DAPO, a trajectory-aware reinforcement fine-tuning algorithm. Experiments on WOMD and nuPlan demonstrate that K-Gen outperforms existing baselines, highlighting the effectiveness of combining multimodal reasoning with keypoint-guided trajectory generation.

Executive Summary

This article proposes K-Gen, an interpretable keypoint-guided multimodal framework for generating realistic and diverse trajectories in autonomous driving simulation. Leveraging Multimodal Large Language Models (MLLMs), K-Gen unifies rasterized BEV map inputs with textual scene descriptions, generating keypoints with reasoning that reflects agent intentions. The framework is further enhanced by T-DAPO, a trajectory-aware reinforcement fine-tuning algorithm. Experiments on WOMD and nuPlan demonstrate K-Gen's effectiveness in outperforming existing baselines. This work highlights the potential of combining multimodal reasoning with keypoint-guided trajectory generation, a crucial aspect of autonomous driving.

Key Points

  • ▸ K-Gen is a multimodal language-conditioned approach for interpretable keypoint-guided trajectory generation
  • ▸ The framework unifies rasterized BEV map inputs with textual scene descriptions using MLLMs
  • ▸ T-DAPO is a trajectory-aware reinforcement fine-tuning algorithm that enhances keypoint generation

Merits

Strength in multimodal reasoning

K-Gen's ability to combine rasterized map inputs and textual scene descriptions using MLLMs demonstrates a novel approach to multimodal reasoning, which is essential for autonomous driving scenarios.

Improvement over existing baselines

Experiments on WOMD and nuPlan demonstrate K-Gen's effectiveness in outperforming existing baselines, highlighting the potential of this framework for real-world applications.

Demerits

Limited generalizability

The framework's reliance on MLLMs and T-DAPO may limit its generalizability to other domains or scenarios, requiring domain-specific fine-tuning.

Computational complexity

The use of MLLMs and trajectory-aware reinforcement learning may introduce significant computational complexity, which could be a bottleneck in real-time applications.

Expert Commentary

K-Gen is a significant contribution to the field of autonomous driving, leveraging multimodal reasoning to generate interpretable keypoints that reflect agent intentions. While the framework demonstrates promising results, its limitations in generalizability and computational complexity must be addressed. Furthermore, the implications of K-Gen's approach for multimodal learning and autonomous driving are far-reaching, with potential applications in human-computer interaction, natural language processing, and computer vision. As the field continues to evolve, it is essential to explore the potential of multimodal reasoning in various domains.

Recommendations

  • ✓ Future research should focus on addressing the limitations of K-Gen, including generalizability and computational complexity.
  • ✓ The framework's potential applications in multimodal learning and autonomous driving should be explored in depth, with a focus on real-world deployment and policy implications.

Sources

Related Articles