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LG-Traj: LLM Guided Pedestrian Trajectory Prediction
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Accurate pedestrian trajectory prediction is crucial for various applications, and it requires a deep understanding of pedestrian motion patterns in dynamic environments. However, existing pedestrian trajectory prediction methods still need more exploration to fully leverage these motion patterns. This paper investigates the possibilities of using Large Language Models (LLMs) to improve pedestrian trajectory prediction tasks by inducing motion cues. We introduce LG-Traj, a novel approach incorporating LLMs to generate motion cues present in pedestrian past/observed trajectories. Our approach also incorporates motion cues present in pedestrian future trajectories by clustering future trajectories of training data using a mixture of Gaussians. These motion cues, along with pedestrian coordinates, facilitate a better understanding of the underlying representation. Furthermore, we utilize singular value decomposition to augment the observed trajectories, incorporating them into the model learning process to further enhance representation learning. Our method employs a transformer-based architecture comprising a motion encoder to model motion patterns and a social decoder to capture social interactions among pedestrians. We demonstrate the effectiveness of our approach on popular pedestrian trajectory prediction benchmarks, namely ETH-UCY and SDD, and present various ablation experiments to validate our approach.
Forward citations
Cited by 7 Pith papers
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Resonance: Learning to Predict Social-Aware Pedestrian Trajectories as Co-Vibrations
A trajectory prediction model that decomposes forecasts into a linear base, a self-sourced vibration, and a social resonance vibration, achieving strong benchmark results with an interpretable decomposition.
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TrajLearn: Trajectory Prediction Learning using Deep Generative Models
TrajLearn predicts future trajectory steps as sequences of hexagon cells using a decoder-only transformer and spatially constrained beam search, reporting up to about 40% relative accuracy gains over baselines.
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Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey
A structured survey of LLM-based trajectory prediction methods, organized into trajectory-language mapping, multimodal fusion, and constraint-based reasoning, with benchmarks, metrics, and future directions.
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Application of Vision-Language Model to Pedestrians Behavior and Scene Understanding in Autonomous Driving
A GPT-4V knowledge distillation pipeline is applied to pedestrian semantic attribute prediction and trajectory forecasting, reporting improved open-vocabulary classification and lower trajectory error on Waymo data.
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Who Walks With You Matters: Perceiving Social Interactions with Groups for Pedestrian Trajectory Prediction
A trajectory prediction model that adds hand-crafted group detection and field-of-view based social perception features improves pedestrian forecasting accuracy on some benchmarks.
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A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach
A position/review paper argues data-driven model predictive control is the best route to safe, adaptive, human-like autonomous-driving motion planning, but provides no new derivation or experiment.
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Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions
The paper surveys recent work, models, applications, and challenges of using LLMs in intelligent transportation systems, without presenting new experimental results.
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