Risk Scenario Generation for Robust Lane Line Detection
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Solution Overview
Problem
Current lane line detection models in autonomous driving systems are affected by various environmental factors, leading to inaccurate predictions and potentially dangerous deviations from the road, as they cannot effectively encompass diverse risk scenarios.
Innovation Solution
A data processing method and apparatus that mine risk factors from driving scenario data to generate a comprehensive and accurate simulated risk scenario, improving the robustness of detection models and safety of autonomous driving systems by identifying and incorporating diverse risk factors into simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional lane line detection models are used, then the system operates with simple structure, but the detection accuracy deteriorates under diverse environmental risk factors
Solution Approach 1:
The patent applies preliminary action by pre-mining risk factors from historical driving scenario data and pre-generating simulated risk scenarios before actual detection operations. The risk factor mining unit extracts critical risk factors in advance, and the simulated scenario generation unit creates diverse test scenarios beforehand, allowing the detection model to be trained and evaluated on comprehensive risk conditions prior to deployment, thereby improving detection accuracy without increasing operational complexity
Solution Approach 2:
The patent segments the detection system into multiple functional modules: risk factor mining unit, simulated scenario generation unit, and detection model training unit. Each module handles a specific aspect of risk analysis and model improvement, allowing the complex detection task to be divided into manageable components that can be optimized independently while maintaining overall system accuracy
2Reliability
If diverse risk scenarios are incorporated into simulation, then the robustness of detection model improves, but the data processing complexity increases
Solution Approach 1:
The patent uses copying by creating simulated risk scenarios that replicate real-world driving conditions without requiring actual physical test data. The simulated scenario generation unit generates virtual representations of various risk situations (adverse weather, complex road conditions, etc.) based on mined risk factors, allowing comprehensive safety testing through data copying rather than requiring diverse physical test environments
Solution Approach 2:
The patent applies parameter changes by systematically varying environmental parameters in simulated scenarios based on mined risk factors. The system adjusts parameters such as weather conditions, road characteristics, and traffic patterns within the simulation to create diverse risk scenarios, enabling robustness testing through controlled parameter variations without proportionally increasing processing complexity
3Adaptability or versatility
If risk factor mining is performed on driving scenario data, then the comprehensiveness of risk scenarios improves, but the processing time increases
Solution Approach 1:
The patent extracts only the critical risk factors from comprehensive driving scenario data rather than processing all available information. The risk factor mining unit identifies and extracts key risk elements (such as adverse weather conditions, road geometry features, traffic patterns) from the broader dataset, achieving comprehensive risk scenario coverage by focusing on essential factors rather than processing complete raw data
Solution Approach 2:
The patent applies partial action by performing risk factor mining on a representative subset of driving scenario data that captures the essential diversity of risk conditions. Rather than exhaustively processing all possible driving scenarios, the system mines risk factors from a carefully selected portion of data that provides sufficient comprehensiveness for robust model training, reducing processing time while maintaining adaptability
Data Source
AI summary
Provided are a data processing method and apparatus, an electronic device, and a medium, and relates to the technical field of computers, and in particular to the field of autonomous driving and intelligent transportation. An implementation is: obtaining risk information in driving scenario data of a driving scenario; determining a risk factor set based on the risk information, where the risk factor set includes a risk factor existing in the driving scenario data; and generating a simulated risk scenario based on the risk factor set, where the simulated risk scenario reflects at least one risk factor in the risk factor set.