Autonomous Vehicle Sensor Blockage Detection
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Solution Overview
Problem
Autonomous vehicles face safety risks due to the difficulty in detecting when imaging sensors, such as lidar, radar, or sonar, malfunction or become blocked, leading to persistent object misses that do not resolve on their own, which can result in accidents or damage.
Innovation Solution
A system that monitors incoming scene data to differentiate between transitory and static object misses, using a flow chart process to generate occupancy maps and identify sensor blockages, allowing for timely intervention to address static issues before they become hazardous.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the autonomous vehicle uses imaging sensors (lidar, radar, sonar) to detect objects, then the vehicle's perception capability is improved, but the system cannot reliably detect when the sensors themselves malfunction or become blocked
Solution Approach 1:
The patent introduces an intermediary monitoring system that uses additional sensors to detect the presence of objects in front of the vehicle's imaging sensors. This intermediary system acts as a mediator to detect sensor blockages by identifying when expected objects (such as vehicles, pedestrians, or road features) are not detected by the primary imaging sensors, thereby resolving the contradiction between maintaining detection accuracy and ensuring system reliability.
2Reliability
If the system continuously monitors scene data to detect sensor blockages, then the safety is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-defining expected object patterns, locations, and characteristics based on the vehicle's trajectory and environmental context. Instead of continuously analyzing all scene data in real-time, the system pre-establishes what objects should be detected and where they should be located, then only monitors for deviations from these pre-determined expectations, thereby reducing computational complexity while maintaining safety.
Solution Approach 2:
The system implements feedback by continuously comparing actual sensor detections against expected object patterns and using this feedback to identify discrepancies that indicate sensor blockages. The feedback loop allows the system to adaptively adjust monitoring based on the vehicle's current operational context, reducing unnecessary processing while maintaining reliable safety detection.
3Loss of information
If the system differentiates between transitory and static object misses, then the false alarm rate is reduced, but the detection algorithm complexity increases
Solution Approach 1:
The patent applies dynamics by implementing a temporal dimension in the detection algorithm that evaluates object misses over time rather than as static events. The system dynamically assesses whether misses are transitory (temporary and likely benign) or static (persistent and indicative of sensor blockage) by monitoring the duration, frequency, and pattern of misses, thereby reducing false alarms through time-based differentiation without requiring overly complex algorithms.
Data Source
AI summary
Systems and methods are described for monitoring detection of objects in a sensor. The system can generate an occupancy array based on scene data corresponding to a scene of a vehicle. The array can include points that represent a location within the environment of the vehicle and an occupancy value that indicates whether an object is detected at that location. The system can modify an occupancy value of at least one point in the array and identify at least one static object miss based on a group of occupancy values of a group of points in the array.