A collision avoidance system corrects preliminary probability densities using map data regions to align predictions with actual road geometry.
An audio frequency induction loop transmits magnetic signals to wearable receivers, addressing quiet electric vehicle detectability.
An AI-driven traffic control system replaces costly in-ground sensors with optical detection to optimize flow at complex multi-branch intersections.
A smart traffic signal system detects approaching vehicle velocity and environmental conditions to calculate real-time stopping distances.
An IoT management platform determines optimal traffic light durations using real-time pedestrian detection data.
Regional segmentation of intersection zones enables precise collision risk assessment, resolving the trade-off between warning timeliness and system complexity.
A wireless push button assembly transmits pedestrian signal requests via Bluetooth Low Energy to a receiver unit.
A control apparatus activates light-emitting devices to display crosswalks when approaching vehicle speed falls below a reference threshold.
Roadside units generate proxy Basic Safety Messages to detect non-equipped vehicles.
A vehicle-to-vehicle network shares sensor data to coordinate passing maneuvers between focus and oncoming vehicles.
A traffic light management system predicts abnormal vehicle trajectories to generate emergency control signals.
A vehicle-mounted camera system masks high-luminance obstacles to calculate total luminance intensity for fog detection.
Adaptive traffic light signals use segmented illumination zones to provide tailored visual cues for drivers at varying distances.
RFID tags and detection nodes identify vehicles at intersections, enabling automatic traffic light control that adapts to real-time flow.
Staring radar detects vehicle trajectories to classify high-risk cars, preventing red-light violations while minimizing false alarms.
Intelligent processor reallocates unused time slices via RFID readers to resolve congestion from fixed cycle inefficiencies.
A traffic management system uses AI to predict vehicle movements across a sensor network for proactive signal timing adjustments.
A dynamic infrastructure monitoring system adjusts signal phases based on real-time detection of vulnerable road user intent and behavior.
A processing system generates an influence graph from probe data to identify venue-related trips and compute traffic impact scores.
A fiber-shaped wearable device detects distress signals and wirelessly transmits alerts to a remote computer.
A road surface marking system uses light emitters to form urging markings that guide pedestrians away from prohibited traffic lanes.
Photogrammetric analysis of synchronized camera feeds extracts precise x, y, z coordinates to resolve manual counting latency and cost bottlenecks.
A remote controlled mobile traffic platform moves along road surfaces to manage vehicle flow without placing flag persons in hazardous lanes.
A vehicle traveling control device adjusts host vehicle position to maximize preceding vehicle communication range.
A forecasting method generates approximated posterior distributions using particle flow algorithms and recurrent neural networks.
A radar and camera system generates point cloud images through distributed signal processing across specialized processors.
Vision sensors read encoded infrastructure data to provide accurate localization, reducing reliance on costly Lidar systems.
Advanced wireless push button identifies crosswalk direction via receiver angle codes.
A vehicle speed induction system calculates effective time intervals to set induction speed ranges for drivers.