mlsanityData to 14 Oct 2026

INTENT: Trajectory Prediction Framework with Intention-Guided Contrastive Clustering

arXiv 2503.04952 ↗First posted 6 Mar 2025cs.AI cs.ROcited by 2

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Abstract (arXiv)

Accurate trajectory prediction of road agents (e.g., pedestrians, vehicles) is an essential prerequisite for various intelligent systems applications, such as autonomous driving and robotic navigation. Recent research highlights the importance of environmental contexts (e.g., maps) and the "multi-modality" of trajectories, leading to increasingly complex model structures. However, real-world deployments require lightweight models that can quickly migrate and adapt to new environments. Additionally, the core motivations of road agents, referred to as their intentions, deserves further exploration. In this study, we advocate that understanding and reasoning road agents' intention plays a key role in trajectory prediction tasks, and the main challenge is that the concept of intention is fuzzy and abstract. To this end, we present INTENT, an efficient intention-guided trajectory prediction model that relies solely on information contained in the road agent's trajectory. Our model distinguishes itself from existing models in several key aspects: (i) We explicitly model road agents' intentions through contrastive clustering, accommodating the fuzziness and abstraction of human intention in their trajectories. (ii) The proposed INTENT is based solely on multi-layer perceptrons (MLPs), resulting in reduced training and inference time, making it very efficient and more suitable for real-world deployment. (iii) By leveraging estimated intentions and an innovative algorithm for transforming trajectory observations, we obtain more robust trajectory representations that lead to superior prediction accuracy. Extensive experiments on real-world trajectory datasets for pedestrians and autonomous vehicles demonstrate the effectiveness and efficiency of INTENT.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 52 of its 84 references that are in the map. First posted on arXiv 6 Mar 2025.

Builds on

52 of 84 references.

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38 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025E3AD: An Emotion-Aware Vision-Language-Action Model for Human-Centric End-to-End Autonomous Drivingcited by 2
  2. 2026Learning Probabilistic Responsibility Allocations for Multi-Agent Interactionscited by 1