Iteris, Inc.

Knight, Inc.

Michael T Whiting

Todd William Kreter

Yan Gao

Dilip Swaminathan

Robert J Hwang

Shashank Jayaram Shivakumar

Manu P Jain

Wing Lam

Peter Cheng Chen

Stephen A Walker

Matthew W Linton

Robert Y Ung

Nader Ayoub

Allison C Palumbo

Brian R Girardot

A platform for visualization of traffic information at an observed roadway or traffic intersection converts data collected from sensors for rendering as dynamic animations on a virtual map of the observed roadway or traffic intersection. The platform parses and curates incoming sensor data from either a single or multiple sensors representing one or more objects at the observed roadway or traffic intersection, and translates at least location data of each object for correlation of the object's movement relative to the observed roadway or traffic intersection. The platform then generates dynamic animations of the movement of each object and displays the animations as an overlay on the virtual map.

Conversion of object-related traffic sensor information at roadways and intersections for virtual dynamic digital representation of objects

A framework for precision traffic analysis estimates vehicle queue length at an observed roadway and calculates vehicle delay for improvements in traffic flow efficiency at a corresponding traffic intersection. The framework identifies a traffic detection area for a roadway at or near the traffic intersection and detects objects in the traffic detection area from sensors located proximate to the roadway. The framework then analyzes sensor data to determine the efficiency of the traffic network and to determine adjustments to timing of various phases of the signal timing plan for the traffic intersection.

Vehicle queue length and traffic delay measurement using sensor data for traffic management in a transportation network

A framework for precision traffic analysis estimates dilemma zone activity to dynamically adjust phase cycle timing for improved efficiency of traffic flow at a roadway intersection. The framework identifies a traffic detection area at or near a traffic intersection, and detects objects in the traffic detection area from sensors located proximate to the traffic intersection. The framework then correlates lapsed phase cycle times for the traffic signal with associations between object characteristics and known speed characteristics for the roadway for a determination of whether there is dilemma zone activity for the remainder of that phase cycle. The framework then determines whether to adjust a timing of the current phase cycle.

Dynamic advanced traffic detection from assessment of dilemma zone activity for enhancement of intersection traffic flow and adjustment of timing of signal phase cycles

An augmented reality visualization framework for precision traffic analysis combines traffic data with content representative of physical-world characteristics, such as conditions in an intersection or roadway in which traffic activity occurs, and images of the intersection and roadway itself. The framework includes a visualization platform that enables display of the combined traffic data and content representing physical-world characteristics, and continual augmentations of such information in response to adjustments directed by users or automatically detected from actions such as user movement, other user manipulation, or movement of the visualization platform itself. The framework enables traffic applications such that a user can, when the platform is interfaced with intersection or roadway equipment, adjust machine activity or signal timing based upon the visualizations or data presented via the visualization platform. The visualization platform may also be configured to present the traffic data, and the content representative of physical-world characteristics, in a virtual reality setting.

Augmented reality system for visualization of traffic information in a transportation environment

A framework for precision traffic analysis combines traffic sensor data from multiple sensor types, combining the strengths of each sensor type within various conditions in an intersection or roadway in which traffic activity occurs. The framework calibrates coordinate systems in images taken of the same area by multiple sensors, so that fields of view from one sensor system are transposed onto fields of view from other sensor systems to fuse the images taken into a combined detection zone, and so that objects are properly detected and classified for enhanced traffic signal control.

Enhanced traffic detection by fusing multiple sensor data

Pedestrian detection and counting for traffic intersection control analyzes characteristics of a field of view of a traffic detection zone to determine a location and size of a pedestrian area, and applies protocols for evaluating pixel content in the field of view to identify individual pedestrians. The location and size of a pedestrian area is determined based either on locations of vehicle and bicycle detection areas or on movement of various objects within the field of view. Automatic pedestrian speed calibration with a region of interest for pedestrian detection is accomplished using lane and other intersection markings in the field of view. Detection and counting further includes identifying a presence, volume, velocity and trajectory of pedestrians in the pedestrian area of the traffic detection zone.

Pedestrian tracking at a traffic intersection to identify vulnerable roadway users for traffic signal timing, pedestrian safety, and traffic intersection control

A vehicular observation and detection apparatus and system incorporates a detection framework using video analysis to differentiate between motorized vehicles and bicycles for improved traffic flow and safety at intersections. The detection framework creates virtual zones overlaid on lanes of a roadway and analyzes input data representing objects in the virtual zones collected from one or more cameras positioned at or near the roadway.

Bicycle differentiation using video data analytics

Calibration of pedestrian speed with detection zone for traffic intersection control

Pedestrian counting and detection at a traffic intersection based on object movement within a field of view

Pedestrian counting and detection at a traffic intersection based on location of vehicle zones

A multi-object zonal traffic detection framework analyzes temporal and spatial information from one or more sensors in a classification engine that identifies and differentiates objects within a single identified traffic detection zone. The classification engine applies a whole scene analysis and an adaptive background zone detection model to classify cars, trucks, bicycles, pedestrians, incidents, and other objects within the single identified traffic detection zone and generates counts for each object type for traffic system management.

Traffic detection with multiple outputs depending on type of object detected

A traffic management apparatus and system performs data processing functions on images in a video data stream to analyze differences between portions of the images and account for movement of a camera at a traffic intersection or other such environment. The traffic management apparatus and system is configured to be placed on a span wire or other non-fixed position at or near the traffic intersection.

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Rancho Santa Margarita, CA, United States of America

Michael T Whiting