Perception Error Distribution Modeling for Autonomous Vehicle Safety Buffers
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Solution Overview
Problem
Conventional error modeling techniques in autonomous vehicles produce inaccurate and unrealistic error values, failing to accurately represent perception data errors, which can result in unsafe navigation and unrealistic simulations.
Innovation Solution
The use of machine learning techniques to train models that output error probability distributions for object attributes such as position, size, and velocity, accounting for correlations between errors, allowing for more accurate representation of potential errors in perception and prediction systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional error modeling techniques are used to determine safety buffers, then the navigation system can operate with simple error assumptions, but the error values produced are inaccurate and unrealistic leading to unsafe navigation
Solution Approach 1:
The patent replaces conventional deterministic error modeling with machine learning-based probabilistic error distributions. Instead of using fixed error assumptions or simple statistical models, the system employs trained neural network models that output probability distributions for perception errors, allowing the navigation system to account for uncertainty in a more accurate and realistic manner.
Solution Approach 2:
The patent transforms error representation from fixed deterministic values to probabilistic distributions with multiple parameters (mean, standard deviation). This allows the system to capture not just the magnitude of errors but also their variability and uncertainty, enabling more sophisticated safety buffer calculations that adapt to different operating conditions.
2Reliability
If large safety buffers are selected to account for perception errors, then collision risk is reduced, but vehicle navigation is hindered and unnecessary delays occur
Solution Approach 1:
The patent makes safety buffers dynamic by calculating them based on real-time probability distributions of perception errors. Instead of using fixed conservative buffers, the system adapts the safety buffer size according to the actual uncertainty in perception data, allowing the vehicle to maintain larger buffers when errors are highly uncertain and smaller buffers when perception is more reliable, thus optimizing both safety and efficiency.
Solution Approach 2:
The system uses feedback from the probability distribution models to continuously adjust safety buffer sizes. The models provide information about perception confidence and error likelihood, which feeds back into the navigation planning to determine appropriate safety margins, creating a closed-loop system that balances safety and efficiency based on actual perception quality.
3Productivity
If simple error assumptions are made in simulation systems, then simulations can run efficiently, but the simulated errors are too large or too small making simulations unhelpful for evaluating control systems
Solution Approach 1:
The patent creates accurate copies of real-world perception error characteristics by training models on actual perception data from autonomous vehicle operations. These trained models then generate synthetic error distributions in simulations that faithfully replicate real perception uncertainties, allowing realistic evaluation of control systems without requiring complex physical test environments.
Solution Approach 2:
The system performs preliminary training of error distribution models using real perception data before running simulations. This pre-computed knowledge of perception error characteristics is then reused across multiple simulations, enabling realistic error injection without the computational burden of re-analyzing real data for each simulation run, thus maintaining both efficiency and realism.
Data Source
AI summary
Techniques for modeling the probability distribution of errors in perception systems are discussed herein. For example, techniques may include modeling error distribution for attributes such as position, size, pose, and velocity of objects detected in an environment, and training a mixture model to output specific error probability distributions based on input features such as object classification, distance to the object, and occlusion. The output of the trained model may be used to control the operation of a vehicle in an environment, generate simulations, perform collision probability analyses, and to mine log data to detect collision risks.