Autonomous Vehicle Route Reconstruction for Accident Cause Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in post-accident analysis due to the complexity of identifying causes, which can stem from various factors including environmental, hardware, and software issues, making it difficult to prevent future accidents.
Innovation Solution
A system and method for post-route analysis that divides a vehicle's path into segments, monitors behavior and driving conditions, stores data in immutable storage, and reconstructs the path to determine the cause of accidents, using blockchain networks or hash graphs for secure and unalterable record-keeping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the autonomous vehicle uses pre-programmed algorithms and decision-making systems to navigate and make real-time decisions, then the vehicle can operate autonomously and adapt to dynamic environments, but it becomes difficult to determine the cause of accidents due to the complexity of environmental factors, hardware issues, and software glitches
Solution Approach 1:
The patent segments the autonomous vehicle system into distinct functional modules (sensors, processors, actuators, software layers) and divides the accident analysis into separate investigation tracks for each segment. This segmentation allows investigators to systematically examine each component's contribution to the accident without being overwhelmed by the system's overall complexity.
Solution Approach 2:
The patent implements preliminary action by continuously collecting and storing operational data, sensor readings, and system states during normal vehicle operation. This pre-captured data is preserved in tamper-evident storage before accidents occur, enabling post-accident reconstruction and analysis without relying on human memory or subjective reports.
2Measurement precision
If the vehicle monitors and records detailed behavior and driving conditions during each segment of the path, then accurate accident analysis becomes possible, but the data storage requirements and system complexity increase
Solution Approach 1:
The patent extracts only the critical data elements necessary for accident analysis (sensor data, command logs, system states) from the vast amount of raw vehicle operational data. This extraction focuses storage resources on the most relevant information while discarding redundant data, reducing storage requirements while maintaining analysis accuracy.
Solution Approach 2:
The patent creates simplified copies or representations of complex operational data in tamper-evident storage. Instead of storing all raw sensor streams and processing logs, the system generates condensed data structures that capture essential state information, making the data more manageable while preserving the information needed for accurate accident reconstruction.
3Reliability
If the system stores all vehicle behavior and driving condition data in immutable storage, then tamper-proof evidence is available for accident investigation, but the storage capacity is consumed and data retrieval time increases
Solution Approach 1:
The patent applies local quality by implementing different storage strategies for different types of data. Critical accident-related data is stored with maximum integrity and immutability, while less critical operational data uses more efficient storage formats. This differentiated approach ensures reliability where needed while optimizing retrieval performance.
Solution Approach 2:
The patent implements partial action by selectively storing and prioritizing retrieval of only the most critical data elements during accident investigation. Rather than retrieving and analyzing all stored data equally, the system focuses on the subset of data most relevant to determining accident cause, reducing retrieval time while maintaining analysis completeness.
Data Source
AI summary
Disclosed herein are system and method for driving organization and subsequent analysis of an autonomous vehicle. In an exemplary aspect, the system and method comprise dividing a path of a vehicle into a plurality of segments based on predetermined conditions; monitoring both behavior of the vehicle and driving conditions during each of the plurality of segments; storing the behavior and the driving conditions in a plurality of records of an immutable storage; determining whether an accident has occurred involving the vehicle; in response to determining that the accident has occurred, retrieving for the plurality of segments the behavior and the driving conditions from the immutable storage; reconstructing the path using the retrieved behavior and the driving conditions and the plurality of segments; and analyzing the reconstructed path to determine a cause of the accident.