Vehicle Perception Perturbation Modeling for Adverse Weather Detection
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Solution Overview
Problem
Current object detection and perception systems for autonomous vehicles fail to consider adverse weather conditions, relying on limited synthetic data and lacking sufficient real-world data for effective operation under conditions like rain, fog, and reduced illumination.
Innovation Solution
An end-to-end perception perturbation modeling system that uses synthetic data to create detection models and vehicle state perturbation models, determining probability values and perturbed states based on weather conditions and distances, integrating with trajectory prediction and motion planning systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-world data is collected for training perception systems under adverse weather conditions, then detection accuracy improves, but time consumption and data collection difficulty increase significantly
Solution Approach 1:
The patent creates synthetic training data by copying and transforming existing clear-weather image data to simulate adverse weather conditions. Virtual images are generated by applying weather effect models (rain, fog, snow, illumination changes) to clear-weather reference images, eliminating the need for time-consuming real-world data collection under each weather condition while maintaining detection accuracy
Solution Approach 2:
The system pre-processes clear-weather reference images to create comprehensive training datasets for all anticipated adverse weather conditions before actual deployment. By beforehand generating virtual images with various weather effects and corresponding perturbed ground truth data, the system prepares perception models in advance, avoiding time-consuming data collection during critical adverse weather events
2Loss of time
If synthetic data is used to train perception systems, then data collection time is reduced, but detection accuracy deteriorates due to limitations in machine-learning techniques
Solution Approach 1:
The patent introduces perturbation models as intermediary components that bridge clear-weather reference data and adverse-weather training data. These models apply physics-based weather effects (precipitation, fog, illumination changes) to transform clear-weather images into realistic adverse-weather scenarios, improving synthetic data quality beyond simple machine-learning generation while maintaining efficiency
Solution Approach 2:
The system transforms training data by changing weather-related parameters (precipitation intensity, fog density, illumination levels, camera noise characteristics) while preserving the underlying scene geometry and object positions. By systematically varying these parameters to create perturbed ground truth data, the system generates diverse training samples that improve detection accuracy without requiring extensive real-world data collection
3Ease of manufacture
If perception systems are trained only on clear weather data, then training simplicity increases, but reliability under adverse weather conditions deteriorates
Solution Approach 1:
The patent creates a universal training framework where a single clear-weather reference dataset serves multiple purposes: it is the source for generating training data for all types of adverse weather conditions (rain, fog, snow, dusk, night). By using perturbation models to transform the reference data, the system achieves multi-functionality, improving reliability across all weather conditions while maintaining training simplicity through a single reference dataset
Data Source
AI summary
An end-to-end perception perturbation modeling system for a vehicle includes one or more controllers storing a detection model in memory. The detection model includes a plurality of detection model plots that each indicate a probability value that an object in an environment surrounding the vehicle is detected based on a current weather condition and a distance measured between the vehicle and the object detected in the environment surrounding the vehicle. The one or more controllers execute instructions to receive an input state of the vehicle that is observed during non-inclement weather conditions and indicates one or more vehicle states, the current weather condition, and the distance. The controllers determine a perturbed state of the vehicle observed during the current weather condition.