Vehicle Detection Using Spatial Sensor Array
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Solution Overview
Problem
Conventional vehicle detection and classification systems, such as inductive sensors and optical techniques, face challenges in distinguishing between vehicles in bumper-to-bumper traffic, detecting smaller vehicles like bicycles and scooters, performing poorly in adverse weather conditions, and requiring calibration for specific speeds and lanes, leading to inefficiencies and inaccuracies.
Innovation Solution
A vehicle detection and classification system utilizing a spatial array of ultra-wideband proximity sensors that communicate using ultra-wideband signals, capable of detecting and classifying vehicles based on their length and width, and optionally determining speed and angle of travel, with sensors spaced to cover multiple lanes and varying weather conditions, and can be implemented either beneath or above the road surface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inductive sensors are used to detect wheel axles, then vehicle counting can be achieved, but the system cannot distinguish between individual vehicles in bumper-to-bumper traffic and detects false positives
Solution Approach 1:
The patent divides the detection task into multiple independent sensor elements arranged in an array, where each sensor provides localized detection data. By segmenting the detection function across multiple sensors rather than using a single inductive sensor, the system can distinguish individual vehicles in traffic queues and reduce false positives through spatial differentiation.
Solution Approach 2:
The patent transitions from single-point inductive detection to a two-dimensional spatial array of sensors. This dimensional expansion allows the system to detect vehicles across multiple positions simultaneously, enabling accurate vehicle counting and classification while eliminating the ambiguity of detecting long singular vehicles in bumper-to-bumper traffic.
2Measurement precision
If inductive loop sensors are used, then vehicle detection can be achieved, but the system cannot detect smaller vehicles like bicycles, scooters and motorcycles
Solution Approach 1:
The patent uses multiple small sensor elements distributed across the road surface rather than a single large inductive loop. This segmentation allows detection of smaller vehicles by providing sufficient spatial resolution to detect the smaller footprint of bicycles, scooters, and motorcycles, while maintaining the ability to detect larger vehicles.
Solution Approach 2:
Each sensor in the array provides localized detection with specific spatial characteristics. The system leverages the local detection capability of individual sensors to identify vehicles of various sizes by analyzing the spatial pattern of detected sensors, enabling versatile vehicle type coverage from small two-wheelers to large trucks.
3Measurement precision
If optical techniques such as laser sensors or cameras are used, then vehicle classification can be achieved, but the system performs poorly in adverse weather conditions such as rain, snow, fog and hot weather
Solution Approach 1:
The patent replaces optical detection systems (cameras and laser sensors) with a different physical detection mechanism - electromagnetic induction sensors. This substitution eliminates the weather-related performance degradation inherent in optical systems, as the inductive sensors detect vehicles through electromagnetic fields that are not blocked by rain, snow, fog, or heat haze, while still enabling vehicle classification through spatial analysis.
4Measurement precision
If inductive loop or laser based systems are used, then vehicle detection can be achieved, but the systems require calibration for particular speed ranges and lane positions
Solution Approach 1:
The patent designs a sensor array system that universally detects vehicles across all lanes and speed ranges without requiring lane-specific or speed-specific calibration. The multi-functional array can detect and classify vehicles in any position and at any speed by analyzing the spatial-temporal pattern of sensor activations, eliminating the need for separate calibration procedures for different operating conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides accurate detection and classification of vehicles in any lane and weather conditions, reducing false positives and enabling efficient tolling and traffic monitoring by distinguishing between different types of vehicles and their axle configurations, while minimizing infrastructure needs and maintaining low power consumption.
Implementation Method 1
a plurality of ultra-wideband proximity sensors distributed in a fixed spatial array... each of said sensors being configured to determine presence or absence of a vehicle and to communicate data regarding said presence determination to a data processing system
Data Source
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AI summary
A vehicle detection and classification system which comprises a plurality of proximity sensors is distributed in a fixed spatial array relative to a road such that a distance of each sensor to the nearest adjacent sensor is less than a minimum horizontal dimension of a vehicle to be detected. The array has a maximum dimension greater than the minimum horizontal dimension of a vehicle to be detected. Each of the sensors is configured to determine presence or absence of a vehicle and to communicate data regarding said presence determination to a data processing system, wherein the data processing system is configured to use data from a plurality of the sensors to detect and classify a vehicle on the road based on at least one dimension of the vehicle.