Remote Vehicle Assistance Image Compression by Map-Based ROI
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently transmitting and processing sensor data, particularly in bandwidth-limited scenarios, where only a portion of the data relevant to the immediate vicinity is necessary for decision-making, leading to potential safety issues in unusual driving situations.
Innovation Solution
The vehicle compresses sensor data by matching features in images with a map, identifying a relevant area within a threshold distance, and reducing detail in non-relevant areas, then transmits the compressed images to a remote system for further processing, allowing for reliable operation instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle transmits full-resolution sensor images to the remote system, then the remote system can analyze all details for decision-making, but the bandwidth requirements and data transmission time increase significantly
Solution Approach 1:
The patent segments the sensor image into multiple regions of interest (ROIs) based on map data and vehicle context. Only these segmented regions containing relevant information (road features, obstacles, traffic signs) are transmitted to the remote system, while other areas are excluded from transmission. This segmentation approach maintains decision-making accuracy for critical areas while significantly reducing overall data transmission volume.
Solution Approach 2:
The patent extracts and transmits only the essential portions of the sensor data that are relevant to safe vehicle operation. By using map data and vehicle state information to identify and extract critical regions (such as areas containing obstacles, road boundaries, or traffic control devices), the system removes unnecessary data from transmission, reducing bandwidth requirements while preserving the information needed for reliable decision-making.
2Productivity
If the vehicle compresses sensor data to reduce bandwidth usage, then data transmission efficiency improves, but critical details may be lost affecting safety in unusual driving situations
Solution Approach 1:
The patent applies different quality levels to different regions of the sensor image based on their importance. Critical regions (such as areas containing obstacles, pedestrians, or traffic control devices) are maintained at high quality or full resolution, while non-critical background areas are compressed or excluded. This local quality differentiation ensures that compression does not eliminate critical details needed for safety while still achieving overall bandwidth reduction.
Solution Approach 2:
The patent performs preliminary analysis of the sensor image using map data and vehicle context information before compression or transmission. By pre-identifying regions of interest and determining which areas contain critical information, the system can apply appropriate compression strategies to each region, ensuring that critical details are preserved while non-critical areas are compressed more aggressively to improve transmission efficiency.
3Speed
If the vehicle processes and analyzes all sensor data locally, then real-time decision-making can be achieved, but the computing power and energy requirements increase
Solution Approach 1:
The patent segments the computational task into two parts: preliminary processing and analysis are performed locally on the vehicle using onboard computing resources, while the refined and segmented data from regions of interest are transmitted to the remote system for further processing. This segmentation of computational workload reduces the energy requirements of onboard processing while maintaining response time through efficient local preprocessing.
Solution Approach 2:
The patent introduces a remote system as an intermediary to handle complex or computationally intensive analysis tasks. Instead of requiring all processing to occur locally on the vehicle, the system transmits processed sensor data to the remote system, which acts as an external computing resource. This intermediary approach reduces onboard energy consumption while still enabling comprehensive analysis for real-time decision-making.
Data Source
AI summary
A vehicle may receive one or more images of an environment of the vehicle. The vehicle may also receive a map of the environment. The vehicle may also match at least one feature in the one or more images with corresponding one or more features in the map. The vehicle may also identify a given area in the one or more images that corresponds to a a portion of the map that is within a threshold distance to the one or more features. The vehicle may also compress the one or more images to include a lower amount of details in areas of the one or more images other than the given area. The vehicle may also provide the compressed images to a remote system, and responsively receive operation instructions from the remote system.