Autonomous Vehicle Parking Permission Detection via Sensor Fusion
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Solution Overview
Problem
Current autonomous vehicle systems fail to identify permissioned parking spaces effectively, especially in dense cities where multiple restrictions and permissions apply, requiring human intervention to navigate and comply with parking laws and regulations.
Innovation Solution
An autonomy controller system that utilizes sensor fusion, including radar, lidar, image, and ultrasound data, to detect and classify objects, combined with map data and parking characteristic information, to autonomously select a collision-free parking path and determine permissioned parking spaces by analyzing signs and restrictions, allowing the vehicle to search for and park in suitable locations without human input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicle systems use basic sensor analysis to identify parking spaces, then the system complexity is reduced, but the ability to identify permissioned parking spaces in dense cities with multiple restrictions fails
Solution Approach 1:
The system segments parking restriction identification into multiple classification levels (e.g., disabled parking, loading zones, fire lanes, time-restricted areas). Each restriction type is detected and evaluated independently through separate logical branches in the control system, allowing comprehensive permission verification without overwhelming system complexity.
Solution Approach 2:
The patent introduces an intermediary processing layer between sensor detection and parking decision-making. This intermediary component analyzes sensor data, cross-references multiple restriction types, and determines permission status before the vehicle commits to a parking space, thereby improving reliability without directly increasing base system complexity.
2Measurement precision
If human drivers manually identify parking permissions, then parking space selection accuracy is improved, but the need for human intervention increases and automation is reduced
Solution Approach 1:
The autonomous vehicle system performs self-service by autonomously detecting parking restrictions, evaluating permission criteria, and selecting appropriate parking spaces without human intervention. The control system independently processes sensor data, applies permission logic, and executes parking maneuvers, thereby maintaining high detection accuracy while maximizing automation.
Solution Approach 2:
The system implements feedback loops where sensor data continuously monitors parking restriction signs and conditions, the control system evaluates permission status based on this feedback, and adjusts parking space selection accordingly. This closed-loop approach enables accurate permission detection while maintaining full autonomous operation.
3Measurement precision
If the vehicle analyzes multiple classes of parking restrictions, then permissioned parking identification accuracy is improved, but the time required to select a parking space increases
Solution Approach 1:
The control system performs preliminary analysis of parking restrictions by pre-processing sensor data to identify and classify restriction types before making parking decisions. By提前 categorizing restrictions (e.g., disabled, loading, fire lane, time-restricted), the system reduces the computational burden during real-time parking selection, thereby maintaining high identification accuracy while reducing overall search time.
4Measurement precision
If advanced sensor fusion is used to detect parking restrictions, then permission identification accuracy is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent merges multiple sensor types (cameras, LIDAR, radar) into a unified sensor fusion system that collectively detects parking restrictions. By combining the strengths of different sensors—visual recognition from cameras, spatial mapping from LIDAR, and penetration capability from radar—the system achieves high detection accuracy while managing complexity through integrated processing rather than separate independent systems.
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
Enables autonomous vehicles to efficiently identify and park in permissioned spaces, reducing the need for human intervention and improving navigation in complex urban environments by accurately determining parking permissions and restrictions using advanced sensor data analysis and path planning algorithms.
Implementation Method 1
sensor fusion, including radar, lidar, image, and ultrasound data
Implementation Method 2
sensor fusion, including radar, lidar, image, and ultrasound data
Implementation Method 3
sensor fusion, including radar, lidar, image, and ultrasound data
Data Source
AI summary
Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computing software, including autonomy applications, image processing applications, cloud storage, cloud computing applications, etc., and computing systems, and wired and wireless network communications to facilitate autonomous control of vehicles, and, more specifically, to systems, devices, and methods configured to identify permissioned parking relative to multiple classes of restricted and privileged parking. In some examples, a method may include determining a parking area for an autonomous vehicle; based at least in part on the parking area, determining a parking place for the autonomous vehicle; determining a parking duration for the autonomous vehicle in the parking place; based at least in part on the parking duration, determining a classification of the parking place; and based at least in part on the classification, initiating a command to position the autonomous vehicle in the parking place.