Road Segment Mapping With Real-Time Scenario Detection
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Solution Overview
Problem
Conventional approaches require significant time and expensive sensor suites to generate detailed maps of geographic regions before scenarios associated with those regions can be identified, limiting the ability of vehicles to navigate safely and efficiently.
Innovation Solution
The system allows vehicles to simultaneously map road segments and determine scenarios in real-time by extracting features from sensor data, such as images from optical cameras, LiDAR, or radar, without the need for precomputed maps, using a mapping and scenario determination module that includes sensor data processing, feature extraction, and scenario prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mapping approaches are used to generate detailed maps before scenario identification, then mapping accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent combines map generation and scenario identification into a single simultaneous process. The scenario identification module operates on sensor data while the map is being constructed, eliminating the sequential dependency where scenario analysis must wait for complete map generation. This merging allows both tasks to progress concurrently, reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The system performs preliminary scenario identification on available sensor data before the complete map is generated. By initiating scenario analysis with partial data early in the process, the system avoids waiting for full map completion, thereby reducing time loss while ensuring accurate scenario detection as more map data becomes available.
2Measurement precision
If conventional mapping approaches are used to generate detailed maps before scenario identification, then mapping accuracy is improved, but system cost increases due to expensive sensor suites
Solution Approach 1:
The system uses the vehicle's existing sensor suite for dual purposes: both for navigation and for scenario identification. By designing the scenario identification module to operate directly on sensor data without requiring separate specialized sensors, the system eliminates the need for expensive additional sensor equipment while maintaining accurate scenario detection capabilities.
Solution Approach 2:
The patent makes the sensor suite multi-functional by enabling it to serve both navigation and scenario identification purposes simultaneously. The same sensors used for basic vehicle operation are leveraged to identify scenarios, eliminating the need for dedicated expensive scenario detection sensors and reducing overall system cost.
3Productivity
If real-time scenario determination is implemented, then navigation efficiency is improved, but processing complexity increases
Solution Approach 1:
The scenario identification process is segmented into distinct modules: sensor data processing, map generation, and scenario identification. Each module handles specific tasks independently, allowing real-time processing without overwhelming complexity. The segmentation enables parallel processing of different data aspects, improving navigation efficiency while managing computational load.
Solution Approach 2:
The system dynamically adjusts processing based on available data and operational context. The scenario identification module operates flexibly on varying amounts of sensor data without requiring complete map generation first, enabling real-time adaptation to changing conditions while maintaining manageable processing complexity through dynamic resource allocation.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can determine sensor data captured by at least one sensor of a vehicle while navigating a road segment. A plurality of features describing the road segment can be extracted from the sensor data. A map representation of the road segment can be determined based at least in part on the sensor data and the plurality of features extracted from the sensor data, the map representation being determined as the vehicle navigates the road segment. While the map representation of the road segment is being determined, at least one scenario associated with the road segment can be determined based at least in part on the map representation and the plurality of features extracted from the sensor data.