Event-Based Sensor Data Logging for Autonomous Vehicles
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Solution Overview
Problem
Autonomous vehicles generate large amounts of sensor data, requiring significant storage capacity and causing computational and financial costs, as well as time-consuming data transfer and network traffic, due to the need to store and offload extensive data sets.
Innovation Solution
Implementing event-based data logging techniques, where a computing device flags and manages sensor data associated with specific events differently than non-event data, using storage rules to determine how and when data is stored and transmitted, allowing for reduced storage needs and optimized data transfer by preserving event-related data while overwriting or compressing non-event data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all sensor data is stored in memory, then complete data availability is achieved, but storage capacity requirements increase significantly
Solution Approach 1:
The patent segments sensor data into two categories: event-related data and non-event data. Event-related data is stored with high retention priority in memory, while non-event data is subject to overwriting when memory capacity is reached. This segmentation allows the system to maintain reliable storage of critical data while limiting total storage requirements.
Solution Approach 2:
The patent applies different storage qualities to different portions of data. Event-related data receives high-quality storage with protection from overwriting, while non-event data receives lower-quality storage that allows overwriting. This local differentiation of storage quality enables the system to prioritize important data without requiring unlimited storage capacity.
2Productivity
If large amounts of sensor data are offloaded to remote computing devices, then data processing capability is improved, but network traffic and transfer time increase significantly
Solution Approach 1:
The patent extracts and transmits only event-related data to remote computing devices, leaving non-event data stored locally or discarded. This extraction approach enables remote processing of critical data without the need to transfer entire datasets, significantly reducing network traffic and transfer time while maintaining processing capability for important events.
Solution Approach 2:
The patent applies partial action by transmitting only a subset of sensor data (event-related portions) rather than complete datasets. This partial transmission approach provides sufficient data for remote processing of critical events while avoiding the time and network resource costs of transferring all generated sensor data.
Data Source
AI summary
Techniques and methods for storing data. For instance, a vehicle can receive sensor data generated by one or more sensors. The vehicle can then detect that an event is occurring and/or has occurred using the sensor data. Based on detecting the event, the vehicle can flag a first portion of the sensor data as corresponding to the event. The vehicle can then store the first portion of the sensor data according to a first storage rule and a second portion of the sensor data according to a second storage rule. In some instances, the storage rules may indicate when sensor data is to be overwritten, lengths of time that the sensor data is to be stored, formats for storing the sensor data, and/or when the sensor data is to be sent to one or more computing devices.