Event-Triggered Vehicle Memory Capture for Post-Accident Fault Analysis
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Solution Overview
Problem
Current autonomous vehicle technologies lack effective methods to determine the cause of accidents, particularly in cases where software navigation or design failures are involved, leading to a need for data storage and analysis post-incident to identify potential faults.
Innovation Solution
A system that rapidly downloads volatile data from autonomous vehicle system memory to non-volatile storage upon detecting an event, such as a collision, and analyzes this data using pattern recognition and machine learning to identify software or design issues, with the ability to transmit findings to a cloud service for further analysis and potential firmware updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If sensor data is stored in volatile memory during autonomous vehicle operation, then data can be rapidly accessed and processed, but data may be lost during power loss or accidents
Solution Approach 1:
The system performs preliminary action by detecting events (accidents, collisions, abnormal operations) and proactively transferring critical sensor data from volatile memory to non-volatile storage before power loss occurs. This ensures data preservation while maintaining the performance benefits of volatile memory during normal operation.
Solution Approach 2:
The patent introduces an intermediary mechanism (event detection system and data transfer controller) that monitors vehicle status and mediates between volatile and non-volatile memory systems. When events are detected, this intermediary triggers the data transfer process, ensuring critical information is preserved without compromising normal system performance.
2Reliability
If all sensor data is continuously stored in non-volatile memory, then data preservation is ensured, but storage capacity is consumed and access speed decreases
Solution Approach 1:
The system extracts only the critical portion of sensor data that is relevant to detected events and transfers it to non-volatile memory. This selective extraction approach preserves data reliability for important information while avoiding the storage overhead of continuously saving all sensor data, thus maintaining storage efficiency.
Solution Approach 2:
Different storage strategies are applied to different portions of data based on their importance. Critical event-related data is transferred to non-volatile memory for preservation, while routine operational data remains in volatile memory for fast access. This local quality differentiation optimizes both reliability and productivity.
3Reliability
If data is transferred from volatile to non-volatile memory after an accident, then data preservation is achieved, but time is lost during the transfer process
Solution Approach 1:
The system performs preliminary action by continuously monitoring for events and preparing for data transfer in advance. When an event is detected, the transfer process is immediately triggered, minimizing the time loss. The system is pre-configured to execute the transfer operation without delay.
Solution Approach 2:
The patent implements a rushed data transfer mechanism that prioritizes speed when events are detected. The system skips normal processing steps and directly transfers critical data from volatile to non-volatile memory as quickly as possible, minimizing the time loss while ensuring data preservation.
Data Source
AI summary
A method includes detecting an event occurring on a vehicle. The vehicle includes at least one computing device that controls at least one operation of the vehicle. The at least one computing device includes a first computing device comprising system memory. In response to detecting the event, data is downloaded from the system memory to a non-volatile memory device of the vehicle. In some cases, a control action for the vehicle is implemented based on analysis of the downloaded data.