Remote Parking System Using Synthetic Driver View
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Solution Overview
Problem
Existing remote control systems for autonomous vehicle parking face connectivity delays, bandwidth issues, and the inability of human operators to visualize the vehicle's perspective, making it difficult to effectively control and park autonomous vehicles.
Innovation Solution
A remote parking system that uses cameras in the parking area to generate a real-time driver's perspective view for human operators, employing image analysis and pre-generated assets to reduce bandwidth and latency, while accounting for communication delays by adjusting the scale of objects in the video feed based on the vehicle's speed and distance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If real-time video streaming is used to provide driver's perspective view, then human operators can see from vehicle perspective, but bandwidth consumption increases significantly
Solution Approach 1:
The system creates a synthetic copy of the driver's perspective view by combining camera images with pre-generated 3D models of vehicles and objects. Instead of transmitting raw camera footage, the system generates a virtual representation that replicates the driver's visual experience, significantly reducing bandwidth requirements while maintaining the essential information needed for remote operation.
Solution Approach 2:
The system pre-generates 3D models and asset libraries of vehicles, objects, and environmental elements before remote operation begins. These pre-rendered assets are stored and quickly assembled during remote parking operations, eliminating the need to process and transmit complex visual data in real-time, thus reducing bandwidth consumption while providing accurate driver's perspective views.
2Loss of information
If high-resolution real-time video is transmitted, then operator visualization is improved, but latency increases due to processing and transmission time
Solution Approach 1:
The system pre-processes and stores 3D models, textures, and object representations before remote operation. During operation, these pre-prepared assets are rapidly assembled and rendered based on the vehicle's current position and orientation, eliminating the need for real-time processing of raw sensor data and significantly reducing latency while maintaining high visualization quality.
Solution Approach 2:
Instead of transmitting and processing actual camera feeds in real-time, the system creates a synthetic copy of the driver's view using pre-generated 3D assets. This approach allows for immediate rendering and display without the processing delays associated with capturing, compressing, transmitting, and decoding real-time video streams, thus reducing latency while preserving visualization quality.
3Area of stationary object
If multiple autonomous vehicles are monitored simultaneously, then parking area coverage is improved, but bandwidth and processing resources increase
Solution Approach 1:
The system divides the parking area into multiple zones, each monitored by dedicated camera systems and processed independently. Each autonomous vehicle's perspective view is generated using only the relevant local 3D assets and camera feeds for its immediate vicinity, rather than processing data for the entire parking area. This segmentation allows multiple vehicles to be monitored simultaneously with reduced bandwidth and processing requirements for each individual vehicle's view.
Solution Approach 2:
The system creates independent synthetic copies of driver's perspective views for each autonomous vehicle using shared pre-generated 3D asset libraries. Rather than transmitting multiple sets of raw camera data, the system generates customized virtual views for each vehicle based on its position and orientation, allowing simultaneous monitoring of multiple vehicles while reusing computational resources and asset data across all vehicles.
4Measurement precision
If communication delays are accounted for by adjusting video feed, then control accuracy is improved, but system complexity increases
Solution Approach 1:
The system pre-calculates and stores communication delay characteristics for different vehicle positions and speeds. Based on the vehicle's current state, the system automatically selects appropriate delay compensation parameters and applies them to the synthesized video feed. This pre-prepared approach allows for accurate delay compensation without requiring complex real-time calculations, thus improving control accuracy while limiting the increase in system complexity.
Data Source
AI summary
Devices, systems, and methods for remote control of autonomous vehicles are disclosed herein. A method may include receiving, by a device, first data indicative of an autonomous vehicle in a parking area, and determining, based on the first data, a location of the autonomous vehicle. The method may include determining, based on a the location, first image data including a representation of an object. The method may include generating second image data based on the first data and the first image data, and presenting the second image data. The method may include receiving an input associated with controlling operation of the autonomous vehicle, and controlling, based on the input, the operation of the autonomous vehicle.