AINeutralarXiv – CS AI · May 117/10
🧠Researchers present MORPH-U, a simulation-based autonomous driving system that integrates Vehicle-to-Everything (V2X) communication with LiDAR/radar/camera sensors while implementing Byzantine-inspired safeguards against forged or delayed messages. The framework uses multi-objective optimization to balance safety, comfort, and responsiveness in high-uncertainty environments, demonstrating resilience against coordinated false-message attacks.
AINeutralarXiv – CS AI · 4d ago6/10
🧠Researchers introduce Kalman Evolve, a framework that uses large language models to discover improved filtering algorithms for state estimation by optimizing both noise parameters and the update structure of classical Kalman filters. The approach addresses performance gaps in nonlinear sensing scenarios like Doppler radar and LiDAR, achieving up to 12% RMSE improvement over standard methods.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers propose HGC-Det, a hyperbolic geometry-based cross-modal distillation framework for 3D object detection that integrates point cloud and image data more effectively. The method addresses modality heterogeneity and spatial misalignment issues through three specialized components and demonstrates improved performance across indoor and outdoor datasets.
AIBullisharXiv – CS AI · May 116/10
🧠Researchers propose an inertial motion learning framework for tracking shared bikes in GNSS-denied environments like urban canyons, combining mechanical constraints with mixture-of-experts models to achieve 12% accuracy improvements over baselines. The system leverages pedaling behavior patterns to dynamically calibrate wheel speed estimates, demonstrating practical viability through real-world deployment data from DiDi's bike-sharing platform.
AINeutralarXiv – CS AI · Apr 146/10
🧠A comprehensive review examines explainable AI methods for human activity recognition (HAR) systems across wearable, ambient, and physiological sensors. The paper addresses the critical gap between deep learning's performance improvements and the opacity that limits real-world deployment, proposing a unified framework for understanding XAI mechanisms in HAR applications.
AINeutralarXiv – CS AI · Apr 106/10
🧠Researchers propose an ethical framework for sensor-fused health AI agents that combine biometric data with large language models. The paper identifies critical risks at the user-facing layer where sensor data is translated into health guidance, arguing that the perceived objectivity of biometrics can mask AI errors and turn them into harmful medical directives.
AIBullisharXiv – CS AI · Mar 36/109
🧠Researchers introduced Wild-Drive, a framework for autonomous off-road driving that combines scene captioning and path planning using multimodal AI. The system addresses challenges in harsh weather conditions through robust sensor fusion and efficient large language models, outperforming existing methods in degraded sensing conditions.
AINeutralarXiv – CS AI · Mar 35/104
🧠Researchers have developed PhysFusion, a new AI framework that combines radar and camera data to improve object detection on water surfaces for unmanned vessels. The system achieves up to 94.8% accuracy by using physics-informed processing to handle challenging maritime conditions like wave clutter and poor visibility.
AINeutralarXiv – CS AI · Mar 24/106
🧠Researchers developed a multimodal gesture recognition system using Apple Watch sensors and custom gloves for hands-free drone and robot control in hazardous environments. The framework achieves performance comparable to vision-based systems while being more computationally efficient and robust to environmental conditions.
AINeutralarXiv – CS AI · Mar 23/106
🧠Researchers developed a new method for real-time sea state estimation that jointly estimates both sea conditions and vessel parameters without requiring prior knowledge of wave-vessel transfer functions. The approach uses a mass-spring-damper model with advanced filtering techniques to achieve performance matching traditional methods that assume complete transfer function knowledge.
