Road Map Generation Using Visual Foundation Models for AD Context
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Solution Overview
Problem
Existing foundation models struggle to effectively leverage rich information from maps, such as road layouts and contextual information like speed limits, for automated driving applications.
Innovation Solution
A trained visual foundation model generates highly accurate road maps with locally allocated contextual information, including road layouts and traffic regulations, using image input data to enhance automated driving systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional foundation models are used for automated driving, then general image processing capabilities are available, but the ability to effectively leverage rich map information such as road layouts and contextual traffic regulations is insufficient
Solution Approach 1:
The patent applies parameter changes by fine-tuning the pre-trained visual foundation model with specific parameters and loss functions designed for map generation tasks. The model transitions from general image processing to specialized road map generation by adjusting its internal parameters through supervised fine-tuning on annotated map data, enabling it to effectively leverage rich map information while maintaining adaptability for automated driving applications
Solution Approach 2:
The patent segments the complex task of map information utilization into distinct components: road layout extraction, contextual information detection (speed limits, traffic regulations), and hierarchical feature organization. This segmentation allows the model to process different types of map information separately and integrate them systematically, improving both information retention and application capability
2Measurement precision
If high-resolution road layouts with precise landmark positioning are generated, then positioning accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by using a pre-trained visual foundation model that has already learned general visual features from large-scale image data. This pre-training serves as preliminary preparation, allowing the model to focus computational resources during fine-tuning on achieving precise landmark positioning rather than learning basic visual features from scratch, thereby reducing overall computational complexity while maintaining high measurement precision
Solution Approach 2:
The patent transitions from processing raw pixel data to generating structured vector representations of road layouts. By changing the dimensional representation from continuous image pixels to discrete geometric primitives (lines, polygons, landmarks with coordinates), the model achieves precise positioning while reducing computational complexity through more efficient data structures and algorithms
3Reliability
If comprehensive contextual information including traffic regulations is integrated into road maps, then safety for automated driving is improved, but the complexity of information processing and integration increases
Solution Approach 1:
The patent applies local quality by associating specific contextual information (traffic regulations, speed limits, right of way rules) with specific locations and features in the road map. Instead of processing all contextual information uniformly, the model identifies and integrates only the relevant regulatory information for each particular road segment or intersection, improving safety while reducing overall processing complexity through localized information handling
Solution Approach 2:
The patent inverts the traditional approach by first generating the geometric road layout structure and then overlaying contextual traffic regulation information onto this established framework. This inversion simplifies integration complexity by providing a stable geometric foundation first, making it easier to systematically add layered contextual information without having to simultaneously process both geometric and regulatory data
Data Source
AI summary
Generation of a road map, in particular appropriate for use in automated driving (AD) of a vehicle. A method comprises a step of receiving image input data. The image input data includes acquired image data representing at least one area which is drivable by a vehicle. The method includes a step of generating a road map based on the received image input data. The generating of the road map is performed by a trained visual foundation model for road map generation, in particular appropriate for use in AD. The generated road map includes a road layout within the at least one area drivable by a vehicle along with locally allocated contextual information in view of applicable traffic regulations.