Dual-Mode AV Mapping for Construction Zone Boundary Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating construction areas due to the dynamic nature of these environments, where high-quality digital maps may be outdated, and perception systems have limited detection ranges, leading to potential misalignment between mapped and perceived boundaries, which can result in inadequate reaction time to changes.
Innovation Solution
A dual mode map system that incorporates sparse map data in construction areas, dynamically augmenting it with perception data to generate accurate pathways, allowing the vehicle to operate safely even without up-to-date dense map data, using a unified boundary machine learning model to detect and manage various types of boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-quality dense digital maps are used to represent static objects and boundaries, then navigation accuracy is improved, but map maintenance overhead and update complexity increase substantially
Solution Approach 1:
The map is segmented into construction areas (with sparse map data) and non-construction areas (with dense map data). This segmentation allows the system to maintain high navigation accuracy in non-construction areas while reducing maintenance overhead in dynamic construction areas where real-time perception supplements the sparse map data.
Solution Approach 2:
The map density is made dynamic based on the environment type. In construction areas, the system transitions from relying on dense pre-collected map data to using sparse map data augmented with real-time perception data, allowing the system to adapt to changing conditions without requiring continuous map updates.
2Adaptability or versatility
If perception systems are used to detect boundaries in real-time, then adaptability to changes is improved, but detection range is limited and reaction time is reduced
Solution Approach 1:
Dense map data is collected and stored in advance for non-construction areas, providing pre-process information about boundaries and static objects. This preliminary action allows the system to navigate these areas without relying solely on real-time perception, thereby increasing reaction time and reducing the burden on the perception system.
Solution Approach 2:
The dual mode map acts as an intermediary between the perception system and the navigation system. In construction areas, the sparse map data combined with real-time perception data serves as a mediator that extends the effective detection range beyond what the perception system alone can achieve, providing earlier warning of boundaries and obstacles.
3Productivity
If sparse map data is used for construction areas, then map update overhead is reduced, but navigation reliability in dynamic environments decreases
Solution Approach 1:
The system merges sparse map data with real-time perception data to create a comprehensive environmental model for construction areas. This combination allows the system to maintain navigation reliability by supplementing the limited sparse map information with current perception data, while still benefiting from reduced map update overhead compared to dense maps.
Solution Approach 2:
The system changes the data density parameter based on the environment type. In construction areas, it uses sparse map data with lower detail, while in non-construction areas, it uses dense map data with higher detail. This parameter change allows the system to optimize between map update efficiency and navigation reliability for different operational contexts.
Data Source
AI summary
An autonomous vehicle control system and method may utilize a dual mode map including sparse map data for some portions of an environment that lacks some of the data maintained in dense map data for other portions of the environment. Sparse map data may be used, for instance, to address a recently-established construction area on a roadway that is incompatible with dense map data that was previously used to operate on the roadway, enabling operation of an autonomous vehicle in the construction area to proceed even in the absence of dense map data for the construction area, e.g., by dynamically augmenting the sparse map data to incorporate additional data sensed by a perception system of the autonomous vehicle.