Dynamic Vehicle Data Compression for Bandwidth Optimization
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Solution Overview
Problem
Current data compression methods for vehicle data are static and do not differentiate between various driving events and vehicle operation conditions, leading to inefficient processing, storage, and transmission.
Innovation Solution
A dynamic data compression system that uses sensors to detect driving events, analyze data streams, and determine vehicle operation conditions, such as speed and location, to dynamically compress data streams from target sensors, adjusting compression based on these conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static data compression is applied to all vehicle data uniformly, then data processing and transmission can proceed with consistent methods, but processing efficiency remains suboptimal and bandwidth usage is not optimized due to lack of differentiation between various driving events and conditions
Solution Approach 1:
The patent implements dynamic data compression by transitioning from static uniform compression to condition-based variable compression. The system continuously monitors vehicle operation conditions (speed, acceleration, location) and adjusts compression parameters in real-time based on the current driving context, enabling optimal processing efficiency for each specific scenario
Solution Approach 2:
The system changes compression parameters dynamically based on vehicle operation conditions. Different compression algorithms and compression ratios are selected according to the current driving state (e.g., highway vs. city driving, normal vs. critical events), optimizing the balance between data fidelity and resource utilization
2Quantity of substance
If high compression ratios are applied to reduce data volume, then bandwidth usage and storage requirements decrease, but processing time and potential loss of critical information may increase
Solution Approach 1:
The patent applies different compression strategies to different data streams based on their importance and characteristics. Critical sensor data (e.g., from collision detection sensors) receives minimal or no compression to preserve reliability, while less critical data (e.g., from ambient temperature sensors) undergoes higher compression ratios, optimizing the balance between data volume reduction and information integrity
3Speed
If data compression is performed continuously at high processing rates, then data can be kept current and up-to-date, but the computing power required and processing burden increase significantly
Solution Approach 1:
The system employs periodic assessment of vehicle operation conditions to determine compression parameters rather than continuous high-rate processing. The compression strategy is recalculated at specific intervals or when significant condition changes occur, reducing the computational burden while maintaining adequate responsiveness to changing driving conditions
Data Source
AI summary
A dynamic data compression system includes a group of sensors and a controller. The sensors are arranged on-board of a vehicle and operable to detect and capture driving event data, the group of sensors comprising a target sensor. The controller is coupled to the group of sensors and operable to receive one or more data streams indicative of the driving event data from the group of sensors. The controller is further operable to (i) analyze the one or more data streams, (ii) determine a vehicle operation condition based on the one or more data streams, the vehicle operating condition comprising a speed of the vehicle, a location of the vehicle, a motion of the vehicle, or a combination thereof, and (iii) determine whether or not to compress a data stream from the target sensor based on the vehicle operation condition.