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Related Experiment Video

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Published on: December 6, 2024

DSC-LLM: Driving Scene Context Representation-Based Trajectory Prediction Framework with Risk Factor Reasoning Using

Sunghun Kim¹, Joobin Jin², Seokjun Hong²

¹Department of AI and Big Data, Soonchunhyang University, 22 Soonchunhyang-ro, Asan 31538, Republic of Korea.

Sensors (Basel, Switzerland)

|December 11, 2025

Summary

This summary is machine-generated.

This study introduces a multimodal framework for accurate autonomous driving trajectory prediction in urban areas. It uses driving scene context (DSC) and a multimodal large language model (MLLM) for interpretable, risk-aware motion explanations.

Keywords:

autonomous driving systems driving scene context multimodal LLM risk-aware reasoning trajectory prediction

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Published on: December 18, 2020

Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Autonomous driving in complex urban environments necessitates precise trajectory forecasting.
Interpretable contextual evidence is crucial for understanding and trusting predicted vehicle motion.

Purpose of the Study:

To develop a multimodal framework for driving scene context (DSC)-aware trajectory prediction.
To provide risk-aware explanations for predicted motion using contextual cues.

Main Methods:

Integration of temporal object states (trajectories, velocities, yaw angles, motion status) with semantic camera imagery.
A four-module framework: behavioral feature extraction, scene context extraction, DSC-augmented prediction, and risk-aware reasoning via a multimodal large language model (MLLM).

Main Results:

The framework achieved competitive performance on the Rank2Tell dataset with ADE of 10.972, FDE of 13.701, and RMSE of 8.782.
Qualitative evaluation indicated DeepSeek-R1-Distill-Qwen-7B provided the most coherent and contextually aligned explanations.

Conclusions:

The proposed approach demonstrates feasibility and applicability for autonomous driving in complex urban settings.
Combining DSC-aware prediction with interpretable reasoning offers a practical and transparent solution for urban autonomous navigation.