Automotive Prediction System Using Temporal Correlation Analysis
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Solution Overview
Problem
Current autonomous vehicle technologies face challenges in achieving Level 5 autonomy, where a vehicle can drive safely in any traffic or weather condition, due to limitations in predicting and responding to dynamic road scenarios effectively.
Innovation Solution
An automotive prediction system that processes road-scene media content sequences to calculate correlations between content descriptors, such as objects and their behaviors, to predict future events and hazards, enabling improved decision-making during vehicle operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous vehicle systems use traditional prediction methods based on current state analysis, then the system complexity remains manageable, but the prediction accuracy and reliability are insufficient for Level 5 autonomy requirements
Solution Approach 1:
The system pre-calculates and stores correlation data between content descriptors and future events during offline processing of media content sequences. This preliminary action allows the runtime prediction system to simply query pre-computed correlations rather than performing complex real-time analysis, thereby achieving high prediction accuracy without excessive runtime complexity
Solution Approach 2:
The patent introduces a temporal dimension by analyzing media content sequences over time and calculating correlations at different temporal distances. This transforms the prediction problem from static state analysis to dynamic temporal pattern recognition, enabling more accurate predictions of future events while maintaining system efficiency through pre-computed temporal correlation matrices
2Reliability
If the system processes extensive road-scene media content sequences to improve prediction reliability, then the prediction reliability improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs extensive processing of road-scene media content sequences, correlation calculations, and pattern recognition during offline training phases. This preliminary processing stores the results in correlation databases that can be quickly queried during runtime, achieving high prediction reliability without excessive real-time processing delays
Solution Approach 2:
The patent segments the prediction task into offline training phase (extensive processing) and online inference phase (quick querying). By dividing the workload temporally and storing intermediate results, the system achieves both high reliability through comprehensive analysis and fast response through efficient query operations
3Measurement precision
If the system calculates correlations at multiple temporal distances to improve prediction accuracy, then the prediction accuracy improves, but the computational complexity increases
Solution Approach 1:
The system pre-calculates correlation data for multiple temporal distances during offline processing and stores these correlations in organized data structures. This allows the runtime system to retrieve pre-computed correlations for different temporal distances without performing repeated complex calculations, achieving high prediction accuracy while maintaining computational efficiency
Data Source
AI summary
In one embodiment, an automotive prediction system includes a processing circuitry to obtain labels labelling media content elements identified in road-scene media content sequences, each label including a content descriptor selected from different content descriptors describing at least one media content element, the different content descriptors including a first and second content descriptor, calculate a correlation of the first and second content descriptor based on a count of occurrences of the first content descriptor being used for labelling after, but within a given temporal distance of the first content descriptor being used for labelling in the road-scene media content sequences, and populate an automotive prediction database with the correlation of the first and second content descriptor for use in making decisions during driving of a vehicle. Related apparatus and methods are also described.