Virtual Sensor Data Generation for Lane Boundary Detection
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Solution Overview
Problem
Acquiring real-world sensor data for lane boundary detection algorithms is expensive and resource-intensive, requiring extensive real-world driving and data collection across various scenarios and conditions, which becomes even more complex with different vehicles and sensor characteristics that change over time.
Innovation Solution
Generating sensor data in a virtual environment using a virtual vehicle with virtual sensors that simulate real-world conditions, allowing for efficient and cost-effective data collection by modeling real-world environments and sensors accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world sensor data are used for developing and training lane boundary detection algorithms, then the accuracy and reliability of the algorithms are improved, but the cost, time and resources required for data acquisition increase significantly
Solution Approach 1:
The patent creates virtual copies of real-world driving environments, vehicles, and sensors in a simulated environment. These virtual replicas generate synthetic sensor data that mimics real-world conditions, allowing algorithm training without physical data collection. The virtual environment includes modeled road surfaces, lane markings, weather conditions, and sensor characteristics that reproduce authentic driving scenarios.
Solution Approach 2:
The patent introduces a simulation environment as an intermediary between real-world conditions and algorithm training. This virtual intermediary translates physical driving scenarios into synthetic sensor data, eliminating the need for direct real-world data collection while preserving the essential characteristics needed for reliable algorithm development.
2Adaptability or versatility
If real-world sensor data are collected across various scenarios and conditions, then the versatility and robustness of the algorithms are improved, but the complexity and resources required for data collection increase
Solution Approach 1:
The patent employs dynamic virtual environments where parameters such as weather conditions, lighting, road surfaces, and traffic scenarios can be changed on-demand. This dynamic capability allows the simulation to generate diverse training data for various driving conditions without requiring physical reconfiguration of data collection equipment or deployment to different real-world locations.
Solution Approach 2:
The virtual simulation environment serves multiple functions: it models various vehicle types, sensor configurations, weather conditions, and road scenarios within a single system. This multi-functional platform can generate training data for different algorithm requirements without needing separate real-world data collection systems for each scenario.
3Reliability
If extensive real-world data collection is performed to account for sensor characteristics changes over time, then the long-term reliability of the algorithms is improved, but the ongoing time and resources required are excessive
Solution Approach 1:
The patent models sensor characteristics and environmental factors in advance within the virtual environment, allowing the system to pre-generate training data that accounts for sensor drift, aging, and environmental variations. This preliminary modeling enables algorithms to be trained on diverse sensor conditions without requiring continuous real-world data collection to capture these changes.
Data Source
AI summary
A method and an apparatus pertaining to generating training data. The method may include executing a simulation process. The simulation process may include traversing one or more virtual sensors over a virtual driving environment defining a plurality of lane markings or virtual objects that are each sensible by the one or more virtual sensors. During the traversing, each of the one or more virtual sensors may be moved with respect to the virtual driving environment as dictated by a vehicle-dynamic model modeling motion of a vehicle driving on a virtual road surface of the virtual driving environment while carrying the one or more virtual sensors. Virtual sensor data characterizing the virtual driving environment may be recorded. The virtual sensor data may correspond to what an actual sensor would produce in a real-world environment that is similar or substantially matching the virtual driving environment.