Dynamic Sensor Data Triggers for Autonomous Vehicle Danger Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in detecting and responding to potentially dangerous situations due to limitations in processing sensor data, which hinders their ability to react appropriately and safely.
Innovation Solution
The implementation of a system that uses dynamic triggers from various sensor systems to identify potentially dangerous events, communicates this information to passengers and remote assistance, and employs machine learning algorithms to determine the severity and necessary responses, including alerting emergency services if required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles process all sensor data continuously to detect dangerous situations, then detection reliability improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments sensor data processing by creating multiple specialized processing pipelines for different sensor types (cameras, LIDAR, radar) and different detection tasks (object detection, trajectory prediction, danger assessment). This modular segmentation allows parallel processing of data streams, improving detection reliability while managing computational complexity through distributed processing architecture.
Solution Approach 2:
The system performs preliminary processing of sensor data including calibration, synchronization, and pre-filtering before main danger detection algorithms are applied. Pre-computed features and pre-processed sensor streams are prepared in advance, reducing the computational burden during critical danger detection moments and improving overall processing efficiency.
2Speed
If the system processes all sensor data in real-time to identify dangerous situations, then response speed improves, but computational load increases
Solution Approach 1:
The patent implements periodic processing cycles with different update frequencies for different data streams based on their importance and temporal characteristics. Critical safety parameters are processed at high frequency, while less critical information is updated at lower frequencies. This periodic action maintains real-time response capability for dangerous situations while reducing overall computational load and energy consumption.
Solution Approach 2:
The system applies partial processing to sensor data by focusing computational resources on the most relevant and critical data streams. Instead of processing all sensor data with equal depth, the system selectively processes data that is most likely to indicate dangerous situations, achieving adequate detection performance with reduced computational load.
3Measurement precision
If the autonomous vehicle system uses multiple sensor systems and complex algorithms to detect dangerous events, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent merges data from multiple sensor systems (cameras, LIDAR, radar, ultrasonic sensors) through sensor fusion algorithms that integrate information at multiple processing levels. This merging approach improves detection accuracy by combining complementary sensor data while managing system complexity through unified processing architectures and shared computational resources.
Solution Approach 2:
The system introduces intermediary processing layers including data association modules, tracking filters, and feature extraction units that mediate between raw sensor data and final danger detection decisions. These intermediaries simplify the overall system architecture by breaking down complex processing into manageable stages, improving detection accuracy without proportionally increasing system complexity.
Data Source
AI summary
A method, non-transitory computer readable medium, and system for receiving sensor data from one or more sensors disposed on an autonomous vehicle, determining whether a potentially dangerous event is detected in the environment around the autonomous vehicle, and providing automatically at least a portion of the sensor data to a user associated with the autonomous vehicle. The sensor data may comprise measurements associated with an environment around the autonomous vehicle. The determination of the potentially dangerous event may be based on the sensor data. The portion of the sensor data may be provided automatically in response to determining that the potentially dangerous event is detected.