Autonomous Vehicle Sensor Visibility Assessment for Degraded Weather
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating environments with varying visibility levels due to weather conditions such as precipitation, fog, or smoke, as sensors perform differently and may struggle to accurately detect objects, affecting the planning and control systems.
Innovation Solution
Implementing a machine-learned model trained on logged sensor data to determine sensor support levels, which generates an output indicating the detectability threshold of reference objects, allowing the autonomous vehicle to adjust its operation based on the sensor support level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sensors are used to detect objects in degraded visibility conditions (precipitation, fog, smoke), then the autonomous vehicle can navigate in various weather conditions, but the sensor detection accuracy and object detectability deteriorate
Solution Approach 1:
The system performs preliminary assessment of sensor support levels by analyzing current sensor data quality before making navigation decisions. The machine-learned model evaluates detectability thresholds in advance, allowing the vehicle to proactively adjust its operation based on predicted detection capabilities rather than reacting to missed detections
Solution Approach 2:
The system dynamically adjusts the autonomous vehicle's operation based on real-time sensor support levels. The machine-learned model continuously evaluates environmental conditions and modifies detection thresholds, planning strategies, and control parameters adaptively, transforming the static sensor system into a dynamic one that responds to changing visibility conditions
2Adaptability or versatility
If the autonomous vehicle operates with reduced sensor support in degraded conditions, then the vehicle can maintain operation in various environments, but the reliability of sensor data and detection confidence decreases
Solution Approach 1:
The system changes operational parameters based on sensor support levels. The machine-learned model adjusts detection thresholds, planning horizons, and control aggressiveness according to environmental conditions, allowing the vehicle to maintain reliable operation across diverse environments by adapting its behavior to match sensor capabilities
Solution Approach 2:
The system implements feedback loops where sensor data quality assessments continuously inform operation adjustments. The machine-learned model evaluates sensor support levels and feeds this information back to the planning and control systems, creating a closed-loop system that maintains reliability through continuous adaptation based on actual sensor performance
3Reliability
If the vehicle adjusts operation based on sensor support levels, then safety and efficiency are improved in degraded conditions, but the complexity of the control system increases
Solution Approach 1:
The machine-learned model acts as an intermediary layer between raw sensor data and the autonomous vehicle's control systems. This intermediary assesses sensor support levels and translates environmental conditions into actionable adjustments for planning and control, managing complexity by providing a standardized interface rather than directly modifying multiple control parameters
Data Source
AI summary
Various examples are directed to systems and methods for operating an autonomous vehicle. The autonomous vehicle may access sensor data captured by at least one sensor corresponding to the autonomous vehicle associated with operation of the autonomous vehicle in an environment. The autonomous vehicle generates an output based on the sensor data and with a machine-learned model. The output may characterize the sensor data to indicate a sensor support level in the environment. The machine-learned model may be trained using training data comprising a plurality of instances of logged sensor data depicting examples of a reference object, each instance of the plurality of instances of logged sensor data being associated with a label indicating a range at which the reference object was detected in the instances of logged sensor data. The autonomous vehicle may be controlled based at least in part on the distance or a visibility classification derived from the distance.