Autonomous Driving Inference Model Refinement via Simulator
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Solution Overview
Problem
Autonomous driving systems face challenges in accurately analyzing certain geographic locations, particularly those with unusual or transient conditions such as snow, construction zones, or irregular vehicles, leading to potential misinterpretation of sensor data and unsafe navigation solutions.
Innovation Solution
A system that collects real-world data from vehicles and uses a driving simulator to refine inference models by comparing simulated responses with human driver actions, with additional data collection from problem locations to improve model accuracy and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous driving systems use standard inference models for navigation, then normal driving operations function adequately, but the system fails to accurately analyze unusual geographic locations such as snow-covered areas, construction zones, or areas with irregular vehicles
Solution Approach 1:
The system performs preliminary actions by dispatching data collection vehicles to identified problem locations before normal operation. These vehicles collect sensor data, images, and video footage of unusual geographic locations (snow, construction zones, irregular vehicles) to build training datasets. This preliminary data collection enables the inference model to be trained in advance on edge cases, improving reliability when encountering these conditions during actual autonomous driving.
Solution Approach 2:
The system implements self-service through automated problem location identification and iterative model refinement. The simulation environment automatically identifies geographic locations where the inference model performs poorly by comparing simulated vehicle responses with human driver responses. The system then autonomously dispatches data collection vehicles, trains improved inference models, and validates results without requiring manual intervention for each problem location, enabling continuous self-improvement of navigation accuracy.
2Measurement precision
If the system collects additional data from problem locations to improve inference models, then model accuracy for unusual conditions improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the essential data needed for model improvement by focusing data collection specifically on identified problem locations. Rather than continuously collecting all possible data, the system targets sensor data, images, and video footage from specific geographic locations where the inference model performs poorly. This selective extraction reduces data processing complexity while maintaining measurement precision for critical edge cases.
Solution Approach 2:
The system adds a new dimension to data collection by deploying mobile data collection vehicles that physically travel to problem locations, rather than relying solely on static sensors or pre-existing datasets. This spatial dimension enables collection of real-world sensor data from actual edge case environments, improving model accuracy for unusual conditions without requiring overly complex processing of exhaustive datasets.
3Reliability
If the system uses simulation environments to test and refine inference models, then model performance can be validated before deployment, but training and validation time increase
Solution Approach 1:
The system uses simulation environments as copies of the real world to test and validate inference models before actual deployment. The simulation recreates geographic locations and driving scenarios, allowing the system to evaluate model performance safely and iteratively. This copying approach enables thorough validation of model improvements without risking safety in real-world testing, maintaining high reliability while reducing the time cost compared to extensive real-world testing.
Data Source
AI summary
Systems, apparatuses, and methods are disclosed for generating an inference model and improving its performance on identified problem locations. Data collected by one or more data sources, such as vehicles, stationary sources, and external or online sources, is used to create an initial inference model, which is then used in an autonomous driving simulator along with the collected data and human driver responses to identify at least one problem location. One or more additional categories of data are identified and collected. The additional category data is then used to create a second inference model.