Dynamic Route Mapping for Autonomous Driving Uncertainty Reduction
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Solution Overview
Problem
Traditional route planning for autonomous and semi-autonomous vehicles is inefficient and costly, requiring extensive data collection to understand driver behaviors in different geographic areas, which can vary significantly.
Innovation Solution
A system and method that generate dynamic maps and routes by approximating initial map information from a database, using sensors to collect and update data, and comparing it to similar datasets to reduce uncertainty in road-agent behavior, allowing for efficient data collection and route optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If arbitrary routes are driven to collect driving data, then driver behavior data can be collected, but time and cost efficiency deteriorates
Solution Approach 1:
The system performs preliminary actions by generating an initial map and identifying high-uncertainty regions before actual data collection. This allows the vehicle to target specific areas that need data collection rather than driving arbitrary routes, thereby reducing time and cost while ensuring reliable driver behavior data is collected from critical regions.
Solution Approach 2:
The system applies local quality by focusing data collection efforts on specific high-uncertainty regions rather than uniformly collecting data across all areas. The uncertainty map identifies localized regions where driver behavior data is most needed, allowing the vehicle to concentrate its data collection efforts where they will have the greatest impact on improving route planning accuracy.
2Reliability
If comprehensive data collection is performed across all geographic areas, then driver behavior understanding is improved, but resource consumption increases
Solution Approach 1:
The system implements local quality by identifying and focusing data collection on specific high-uncertainty regions rather than performing comprehensive data collection across all geographic areas. The uncertainty map allows the system to allocate resources efficiently by targeting only those regions where additional driver behavior data will most significantly improve understanding and route planning reliability.
Solution Approach 2:
The system applies partial action by collecting data only in regions where uncertainty exceeds a threshold rather than performing exhaustive data collection everywhere. This partial approach to data collection maintains reliable driver behavior understanding in critical areas while conserving computational and temporal resources that would be spent on redundant data collection in already-well-understood regions.
3Measurement precision
If dynamic map updating is implemented, then route planning accuracy is improved, but computational complexity increases
Solution Approach 1:
The system reduces computational complexity by applying local quality to the map updating process. Instead of recalculating the entire map structure globally, the system performs localized updates only in regions where new driver behavior data has significantly reduced uncertainty. This localized approach maintains high route planning accuracy in updated regions while avoiding the computational burden of global map recalculation.
Solution Approach 2:
The system implements segmentation by dividing the geographic area into regions based on uncertainty levels. The map is effectively segmented into high-uncertainty regions requiring updates and low-uncertainty regions that can remain unchanged. This segmentation allows the computational system to process only the necessary portions of the map, reducing overall computational complexity while maintaining accuracy where it matters most.
Data Source
AI summary
Systems and methods for generating efficient planning routes for vehicles, including autonomous and semi-autonomous vehicles are presented. A route planner may generate dynamic maps and routes that reduces the uncertainty of road-agent environmental and behavioral data in an efficient manner. Route planning may be accomplished using a statistical approach in which known data from one geographic or behavioral feature set may be used and relied upon by a vehicle in another geographical and behavioral context to estimate the environmental and behavioral data relevant to the vehicles current operation.