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#trajectory-generation News & Analysis

9 articles tagged with #trajectory-generation. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

9 articles
AIBullisharXiv – CS AI · Apr 137/10
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Advantage-Guided Diffusion for Model-Based Reinforcement Learning

Researchers propose Advantage-Guided Diffusion (AGD-MBRL), a novel approach that improves model-based reinforcement learning by using advantage estimates to guide diffusion models during trajectory generation. The method addresses the short-horizon myopia problem in existing diffusion-based world models and demonstrates 2x performance improvements over current baselines on MuJoCo control tasks.

AINeutralarXiv – CS AI · Jun 105/10
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Monte Carlo Pass Search: Using Trajectory Generation for 3D Counterfactual Pass Evaluation in Football

Researchers introduce Monte Carlo Pass Search (MCPS), a novel AI system that evaluates football passes by simulating counterfactual scenarios using trajectory generation and value prediction models. The work combines existing machine learning techniques with a new public Bundesliga dataset featuring 3D ball tracking, enabling distribution-aware analysis of pass execution quality and decision-making.

AINeutralarXiv – CS AI · Jun 106/10
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Diffusion Forcing Planner: History-Annealed Planning with Time-Dependent Guidance for Autonomous Driving

Researchers propose Diffusion Forcing Planner (DFP), a new diffusion-based motion planning framework for autonomous driving that addresses temporal inconsistency in learning-based planners. By decomposing trajectories into history, current, and future segments with independent noise levels and applying annealed guidance, DFP produces more stable and controllable driving plans while avoiding the tendency to simply copy historical patterns.

AINeutralarXiv – CS AI · Jun 26/10
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CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation

Researchers introduce CityTrajBench, a unified benchmark framework for evaluating vehicle trajectory generation models across urban environments. The framework standardizes datasets, preprocessing, and evaluation metrics to enable fair comparison of statistical, VAE, GAN, diffusion, and flow-matching models, revealing that no single approach dominates all quality criteria.

AINeutralarXiv – CS AI · May 296/10
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From GPS Points to Travel Patterns: Flexible and Semantic Trajectory Generation with LLMs

Researchers propose HTP, an LLM-based framework that generates realistic urban trajectories by first synthesizing travel patterns and then GPS points, addressing privacy concerns in smart city applications. The method outperforms existing approaches by 29.78% and can generate variable-length trajectories under multiple conditions, advancing synthetic data generation for urban analytics.

AINeutralarXiv – CS AI · May 296/10
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Stochastic Lifting for Generating Trajectories of Stochastic Physical Systems

Researchers introduce Stochastic Lifting, a machine learning technique that generates diverse trajectories of stochastic physical systems by attaching random labels to state transitions during training. The method enables single-network inference to produce multiple plausible outcomes without collapsing to average predictions, advancing physics-informed AI applications.

AIBullisharXiv – CS AI · May 126/10
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Latency Analysis and Optimization of Alpamayo 1 via Efficient Trajectory Generation

Researchers have optimized Alpamayo 1, a reasoning-based autonomous driving system, by redesigning it from multi-reasoning to single-reasoning architecture while accelerating diffusion-based action generation. The optimization achieves a 69.23% latency reduction while maintaining trajectory diversity and prediction quality, demonstrating that system-level efficiency improvements are critical for practical autonomous driving deployment.

AINeutralarXiv – CS AI · May 126/10
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diffGHOST: Diffusion based Generative Hedged Oblivious Synthetic Trajectories

diffGHOST is a new conditional diffusion model that synthesizes mobility trajectories while preserving privacy through latent space segmentation. The approach addresses a critical gap in existing generative models that lack formal privacy guarantees despite handling sensitive personal movement data.