AIBearisharXiv – CS AI · Jun 237/10
🧠Researchers demonstrate that transformer-based trajectory prediction models used in autonomous vehicles experience severe accuracy degradation when exposed to noisy real-world sensor data, with prediction accuracy declining by up to 3.9x under realistic noise conditions. The findings highlight a critical gap between idealized training environments and actual deployment scenarios, signaling the need for robust noise mitigation strategies in autonomous vehicle systems.
AIBullisharXiv – CS AI · Jun 27/10
🧠Researchers propose a Risk Horizon Profiling (RHP) module that improves vehicle trajectory prediction for autonomous driving by dynamically modeling future risk distributions rather than relying solely on historical risk data. The method achieves 25-29% error reduction on highway and urban datasets, suggesting significant safety improvements for autonomous vehicles and driver-assistance systems.
AIBullisharXiv – CS AI · May 297/10
🧠Researchers introduce BitTP, a quantization technique that compresses LLM-based trajectory prediction models to 1.58-bit weights while maintaining full-precision activations, enabling deployment on resource-constrained edge devices. The approach not only reduces memory and latency but actually improves prediction accuracy by 14-21% compared to full-precision baselines, demonstrating that strategic quantization can serve as an effective regularizer.
AINeutralarXiv – CS AI · Jun 256/10
🧠AeroCast presents a novel AI framework combining Transformer neural networks with Mixture Density Networks to predict probabilistic 3D trajectories of non-cooperative aerial obstacles. The system achieves 50% error reduction compared to existing methods while maintaining real-time performance at 100Hz, enabling safer autonomous aerial vehicle operations in shared airspace.
AINeutralarXiv – CS AI · Jun 236/10
🧠Researchers submitted a vision-language-action driving agent called OmniDrive to the doScenes Instructed Driving Challenge, which predicts autonomous vehicle trajectories based on visual context, motion history, and natural language instructions. The team introduced a divided-view perception module that improves multi-camera visual grounding by reducing cross-view interference, enabling better alignment between language instructions and driving-relevant visual evidence.
AINeutralarXiv – CS AI · Jun 116/10
🧠ConsistencyPlanner introduces a real-time planning framework for autonomous driving that combines fast-sampling consistency models with heterogeneous feature fusion to balance multimodal driving behavior prediction and computational efficiency. The approach demonstrates improved safety metrics in the Waymax simulator compared to existing methods, addressing a key limitation in learning-based autonomous driving systems.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers develop a large language model framework for predicting vessel trajectories and destinations up to 30 days in advance using reinforcement learning with verifiable rewards. The approach outperforms traditional deep learning methods by maintaining route feasibility and destination accuracy over extended maritime forecasting horizons.
AINeutralarXiv – CS AI · Jun 56/10
🧠Researchers demonstrate that memory-augmented neural networks significantly improve vessel trajectory prediction using AIS maritime data from the Gulf of Mexico and New York Bight. The approach selectively retrieves relevant historical information to outperform conventional deep learning models, with applications for collision avoidance and maritime route optimization.
AINeutralarXiv – CS AI · Jun 15/10
🧠Researchers propose SKETCH, a semantic key-point-conditioned framework that improves long-horizon vessel trajectory prediction by decomposing the problem into high-level navigational intent and local motion modeling. The method outperforms existing approaches on real-world AIS data, particularly for extended time horizons and directional accuracy.
AINeutralarXiv – CS AI · May 286/10
🧠Researchers introduce STFlow, a machine learning model that improves trajectory simulation for complex dynamical systems by using graph neural networks and data-dependent couplings within a Flow Matching framework. The approach outperforms existing methods on molecular dynamics, N-body systems, and pedestrian forecasting with fewer simulation steps and lower computational costs.
AINeutralarXiv – CS AI · May 276/10
🧠Researchers introduce CmIVTP, a cross-modal AI framework that combines AIS and CCTV data to improve maritime vessel trajectory prediction. The system uses transformer-based architecture with attention mechanisms to model vessel-environment interactions, addressing limitations of single-source data in maritime navigation systems.
AIBullisharXiv – CS AI · Mar 96/10
🧠Researchers developed XR-DT, an Extended Reality-enhanced Digital Twin framework that combines augmented, virtual, and mixed reality to improve human-robot interaction in shared workspaces. The system uses a novel Human-Aware Model Predictive Path Integral control model with ATLAS, a Transformer-based trajectory prediction system, to enable safer and more interpretable robot navigation around humans.
AINeutralarXiv – CS AI · Mar 45/103
🧠Researchers propose ShipTraj-R1, a novel LLM-based framework using group relative policy optimization (GRPO) for ship trajectory prediction. The system reformulates trajectory prediction as a text-to-text generation problem and demonstrates superior performance compared to existing deep learning baselines on real-world maritime datasets.
AINeutralarXiv – CS AI · Mar 175/10
🧠Researchers propose TrajMamba, a new AI model that uses Mamba architecture to predict pedestrian movement from an ego-centric perspective for autonomous driving applications. The model integrates pedestrian motion and ego-vehicle movement data to achieve state-of-the-art performance on PIE and JAAD datasets.
AINeutralarXiv – CS AI · Mar 54/10
🧠Researchers developed TPK, a trajectory prediction system for autonomous vehicles that integrates prior knowledge to make predictions more trustworthy and physically feasible. The system incorporates interaction and kinematic models for vehicles, pedestrians, and cyclists, improving interpretability while ensuring predictions adhere to physics.
