Sequence Inference for Multi-Sensor Driving Event Classification
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Solution Overview
Problem
Current ADAS and autonomous driving systems struggle to accurately detect and classify temporally extended driving events, leading to inefficiencies and false alarms, particularly in complex environments, due to reliance on unreliable sensors and varying performance conditions.
Innovation Solution
A method and apparatus utilizing a sequence inference engine that combines inferences from multiple sensors, including visual data from cameras and other modalities, to determine and classify driving events by processing data vectors of predetermined size, employing neural networks and heuristics to improve detection and classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors and data sources are combined to improve detection accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent combines data from multiple sensors (cameras, LIDAR, radar, GPS, inertial sensors) and processing methods (neural networks, heuristics, sequence inference) into a unified detection system. This merging of multiple data sources and processing approaches enables the system to achieve high detection accuracy for temporally extended events while managing the inherent complexity through integrated architecture.
2Reliability
If sensor data processing is enhanced to reduce false alarms, then reliability improves, but use of energy increases
Solution Approach 1:
The system performs preliminary processing of sensor data through neural networks and heuristic filters before final classification. By pre-processing data and identifying likely events in advance, the system reduces the computational burden on the sequence inference engine, thereby reducing false alarms while managing energy consumption more efficiently.
Solution Approach 2:
The patent applies multiple layers of processing (neural networks, heuristics, sequence inference) that go beyond what a single method could provide. This partial application of multiple processing stages allows the system to achieve high reliability by catching false alarms at different processing levels, while avoiding the need to apply all processing methods at full intensity simultaneously, thus managing energy use.
3Measurement precision
If temporal sequence analysis is performed to classify driving events accurately, then measurement precision improves, but loss of time increases
Solution Approach 1:
The system performs preliminary analysis of sensor data streams using neural networks and heuristics to identify potential events and their temporal patterns before submitting to the sequence inference engine. This preliminary action pre-processes the temporal sequence data, reducing the computational burden on the final classification stage and minimizing processing time while maintaining accuracy.
4Reliability
If comprehensive driver behavior monitoring is implemented, then reliability of driver assessment improves, but device complexity increases
Solution Approach 1:
The patent creates a multi-functional sequence inference engine that handles multiple types of driving events (tailgating, lane changes, intersections, pedestrians) and multiple sensor modalities through a single unified architecture. This universal approach improves driver assessment reliability across diverse scenarios while avoiding the complexity of separate specialized systems for each function.
Data Source
AI summary
Systems, devices and methods provide, implement, and use vision-based methods of sequence inference for a device affixed to a vehicle.