Vehicle Camera Depth Mapping for Parking With Blocked Views
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Solution Overview
Problem
Users face challenges with limited visibility during parking and reversing, leading to a higher risk of collisions and parking accidents due to blocked cameras or sensors, blind spots, and difficulties in judging distances.
Innovation Solution
A system that uses a camera and pre-trained depth algorithms to generate a depth map of the vehicle's environment, excluding objects attached to the vehicle, to determine the distance between the vehicle and parking spaces and output alerts when the distance is less than a threshold value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cameras or sensors are used for environment detection, then detection capability is improved, but visibility is blocked by objects attached to the vehicle
Solution Approach 1:
The system extracts and identifies objects attached to the vehicle (such as bike racks, cargo carriers, or other removable attachments) from the image data. By detecting these objects and excluding them from the analysis, the system removes the blocking elements that prevent cameras and sensors from properly detecting the environment, thereby resolving the contradiction between detection capability and visibility loss
Solution Approach 2:
The system introduces an intermediary processing step that generates a depth map and identifies attached objects as a mediator between the camera/sensor input and the final environment detection output. This intermediary layer allows the system to filter out blocking objects and provide accurate distance measurements to parking spaces and obstacles despite the presence of attached objects that physically block the camera view
2Device complexity
If depth estimation is performed including attached objects, then processing is simplified, but distance measurement accuracy deteriorates
Solution Approach 1:
The system segments the image data into different regions: areas occupied by attached objects and areas representing the actual environment. By dividing the processing into these distinct segments, the system can exclude the attached objects from depth calculations while maintaining simplified overall processing. This segmentation allows accurate distance measurements to parking spaces and obstacles by only considering relevant environmental features
Solution Approach 2:
The system extracts and removes data corresponding to attached objects from the depth map generation process. By taking out the blocking objects from the analysis, the system maintains simplified processing workflows while improving measurement accuracy, as the depth estimation is performed only on the visible environment rather than including fictitious depth information from attached objects
3Productivity
If traditional parking assistance systems are used, then basic detection is provided, but collision risk increases due to blocked views and blind spots
Solution Approach 1:
The system introduces an intermediary object identification and exclusion process that acts as a mediator between traditional parking assistance systems and modern depth estimation technology. This intermediary layer identifies attached objects that create blind spots and blocked views, excludes them from analysis, and provides corrected distance measurements, thereby reducing collision risk while maintaining parking efficiency
Solution Approach 2:
The system replaces traditional mechanical or simple optical parking assistance mechanisms with advanced depth estimation technology using machine learning algorithms. By substituting the older system with this intelligent approach, the system can compensate for blocked views and blind spots caused by attached objects, providing accurate environment detection and reducing collision risk while improving overall parking efficiency
Data Source
AI summary
Embodiments of systems and methods for vehicle environment detection include a vehicle and one or more processors. The vehicle includes a camera operable to generate an image of an environment surrounding the vehicle. The environment includes one or more parking spaces and an object removably attached to the vehicle. The one or more processors are operable to identify the object, generate, using a pre-trained depth algorithm, a depth map based on the image, generate a boundary of the parking spaces based on the depth map excluding the object, determine whether a distance between the boundary of the parking spaces and the vehicle is less than a threshold value, and output an alert in response to determining that the distance is less than the threshold value.