# SemAgent: Semantic-Driven Agentic AI Empowered Trajectory Prediction in Vehicular Networks

arXiv: https://arxiv.org/abs/2512.00834 · HTML: https://mlsanity.com/paper/2512.00834/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 30 Nov 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 12 of 12)

- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey](https://mlsanity.com/paper/2404.11584/) (2024)
- [SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models](https://mlsanity.com/paper/2309.10062/) (2023)
- [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025)
- [Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools](https://mlsanity.com/paper/2502.04644/) (2025)
- [GameBench: Evaluating Strategic Reasoning Abilities of LLM Agents](https://mlsanity.com/paper/2406.06613/) (2024)
- [AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs](https://mlsanity.com/paper/2507.08616/) (2025)
- [OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction System](https://mlsanity.com/paper/2412.20005/) (2024)
- [Importance-Aware Image Segmentation-based Semantic Communication for Autonomous Driving](https://mlsanity.com/paper/2401.10153/) (2024)
- [Semantic Communication for Edge Intelligence Enabled Autonomous Driving System](https://mlsanity.com/paper/2406.10606/) (2024)
- [Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2303.02880/) (2023)

## Built on by (most cited first, 1 of 1)

- [Security Risks of Agentic Vehicles: A Systematic Analysis of Cognitive and Cross-Layer Threats](https://mlsanity.com/paper/2512.17041/) (2025)

## Abstract (arXiv)

Efficient information exchange and reliable contextual reasoning are essential for vehicle-to-everything (V2X) networks. Conventional communication schemes often incur significant transmission overhead and latency, while existing trajectory prediction models generally lack environmental perception and logical inference capabilities. This paper presents a trajectory prediction framework that integrates semantic communication with Agentic AI to enhance predictive performance in vehicular environments. In vehicle-to-infrastructure (V2I) communication, a feature-extraction agent at the Roadside Unit (RSU) derives compact representations from historical vehicle trajectories, followed by semantic reasoning performed by a semantic-analysis agent. The RSU then transmits both feature representations and semantic insights to the target vehicle via semantic communication, enabling the vehicle to predict future trajectories by combining received semantics with its own historical data. In vehicle-to-vehicle (V2V) communication, each vehicle performs local feature extraction and semantic analysis while receiving predicted trajectories from neighboring vehicles, and jointly utilizes this information for its own trajectory prediction. Extensive experiments across diverse communication conditions demonstrate that the proposed method significantly outperforms baseline schemes, achieving up to a 47.5% improvement in prediction accuracy under low signal-to-noise ratio (SNR) conditions.