Automated Parking Area Identification Through Temporal Occupancy Analysis
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Solution Overview
Problem
Existing methods for identifying parking and non-parking areas are inefficient and lack accuracy in vehicle automation, particularly in highly and fully automated driving systems, as they rely on sensor data that is not adequately processed to distinguish between stable and dynamic occupancy changes.
Innovation Solution
A method involving multiple vehicle passes along a road section, capturing parking space data with sensors, accumulating this data into a set, determining temporal change rates, and classifying areas based on these rates to differentiate between parking and non-parking spaces, using ultrasonic or radar sensors and potentially a central server for data aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is collected during single vehicle passages to identify parking areas, then data collection time is reduced, but measurement precision and reliability are insufficient due to inadequate sampling
Solution Approach 1:
The system performs preliminary data collection during multiple vehicle passages through a road section before making parking area identification decisions. By accumulating sensor data from multiple passages (at least two) in advance, the system builds a comprehensive data set that captures temporal variations in parking space occupancy, enabling more accurate identification while distributing the time cost across multiple trips rather than requiring extensive single-trip sampling
Solution Approach 2:
The system employs periodic sampling of parking space data during successive vehicle passages through the same road section. By collecting data at regular intervals (each passage) and analyzing temporal change rates between these periodic measurements, the system achieves reliable parking area identification through repeated observation of occupancy patterns over time
2Measurement precision
If multiple vehicle passes are used to collect parking space data, then measurement precision improves through temporal analysis, but productivity decreases due to increased data accumulation time
Solution Approach 1:
The system extracts and analyzes only the temporal change rate component from the complete sensor data set collected during multiple passages. By focusing computational efforts on calculating temporal derivatives (change rates) rather than processing all raw sensor data, the system achieves accurate parking area identification while reducing overall data processing requirements and improving efficiency
Solution Approach 2:
The system transforms the raw parking space occupancy data into temporal change rate parameters by calculating the derivative of occupancy state over time. This parameter transformation converts static occupancy snapshots into dynamic trend information, enabling more accurate parking area classification while working with a condensed representation that reduces processing complexity
3Manufacturing precision
If temporal change rate analysis is applied to distinguish parking from non-parking areas, then manufacturing precision of digital maps improves, but device complexity increases
Solution Approach 1:
The system replaces complex manual or rule-based methods for creating digital maps with an automated computational approach that calculates temporal change rates from sensor data. This substitution of mechanical/manual processes with algorithmic processing achieves high-precision digital map generation while the automation itself manages the complexity through standardized computational procedures
4Reliability
If parking space data from multiple passages are accumulated and analyzed, then reliability of parking area identification improves, but loss of time increases due to extended monitoring period
Solution Approach 1:
The system performs preliminary data accumulation during multiple vehicle passages before final parking area identification is made. By pre-collecting sensor data from at least two passages and storing it in a data set for later analysis, the system ensures reliable identification results while the time investment is distributed across multiple trips rather than concentrated in a single extended monitoring period
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
Enhances the accuracy and timeliness of identifying parking and non-parking areas by leveraging temporal stability of occupancy changes, enabling precise digital mapping and separation of these zones, suitable for various vehicle types.
Implementation Method 1
using ultrasonic or radar sensors
Implementation Method 2
using ultrasonic or radar sensors
Data Source
Figure 1
AI summary
The invention relates to a method for automatically identifying parking areas and/or non-parking areas, comprising the following steps: S1 carrying out a plurality of passes, but at least two passes, through at least one road section by at least one measurement vehicle; S2 capturing parking space data along the road section during each pass by means of at least one sensor of the at least one measurement vehicle, wherein the parking space data comprise detected parking spaces and respective associated location and time information; S3 accumulating the parking space data captured during each pass in a data record; S4 determining a temporal rate of change for detected parking spaces on the basis of the data record; and S5 determining valid parking areas and/or non-parking areas along the road section on the basis of the temporal rate of change for different detected parking spaces. The invention also relates to a corresponding apparatus for automatically identifying parking areas and/or non-parking areas and to a system and a computer program.