Bayesian Network for Vehicle Sensor Blockage Detection
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Solution Overview
Problem
Current sensor blockage detection methods in vehicles are inadequate, often failing to promptly recognize limitations in a sensor's field of view due to environmental conditions like snow, fog, or direct sunlight, which can lead to safety issues in autonomous and automated driving by inaccurately representing the environment.
Innovation Solution
A Bayesian network is generated using prior and conditional probabilities to detect sensor blockage, incorporating historical data and environmental conditions, with nodes representing sensor blockage and target presence, allowing for real-time detection and environment model updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic sensor blockage detection techniques are used, then device complexity is reduced, but detection precision and reliability are insufficient to meet safety requirements
Solution Approach 1:
The patent introduces a Bayesian network as an intermediary computational model that processes sensor data to infer blockage status. This mediator layer transforms raw sensor observations into probabilistic blockage assessments, achieving high detection precision without requiring complex hardware modifications. The Bayesian network serves as a software-based intermediary that bridges simple sensors and safety-critical decision-making.
Solution Approach 2:
The system changes the parameter representation from deterministic sensor readings to probabilistic blockage likelihoods. By computing posterior probabilities P(blockage|sensor_data) and comparing them against thresholds, the system achieves precise blockage detection through parameter transformation rather than through complex physical sensing mechanisms.
2Speed
If traditional sensor blockage detection methods are used, then system simplicity is maintained, but detection speed is insufficient (requiring multiple seconds to recognize blockage)
Solution Approach 1:
The Bayesian network performs continuous probabilistic inference on incoming sensor data streams, maintaining up-to-date blockage probability assessments without discrete detection cycles. This continuous computation enables real-time blockage detection speed while using standard sensor hardware, avoiding the need for specialized fast-detection equipment.
Solution Approach 2:
The system implements feedback loops where sensor observations continuously update the Bayesian network's belief state about blockage status. This feedback mechanism enables rapid adaptation to changing environmental conditions and immediate blockage detection when sensor performance degrades, achieving high detection speed through iterative probability refinement.
Data Source
Figure 1~3
AI summary
Provided is a method for generating a Bayesian network configured to detect blockage of a plurality of sensors of a vehicle. The method includes setting a set of a priori probabilities including an a priori probability that a target exists in a field of view of at least one, some or all of the plurality of sensors; for each one of the plurality of sensors, respectively, an a priori probability that the sensor is blocked; and for each one of the plurality of sensors, respectively, an a priori probability that the sensor is not blocked. The method further includes setting a set of conditional probabilities each comprising a probability that the sensor of the plurality of sensors will detect and not detect the target in the sensor's field of view among all possible combinations of blocked and unblocked sensor and presence or absence of the target in the sensor's field of view; and generating the Bayesian network based on the set of a priori probabilities and the set of conditional probabilities.