Vehicle Track-Based Map Anomaly Detection for Road Closures
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Solution Overview
Problem
Autonomous vehicles face challenges in detecting and responding to dynamic changes in their environment, particularly construction-related road closures, due to limitations in perception systems and high-quality digital maps, which can lead to inadequate reaction time and incorrect trajectory planning.
Innovation Solution
The system detects non-ego vehicle tracks using sensors to identify roadway closure events by analyzing the trajectories of other vehicles on the road, allowing for early initiation of corrective actions before the autonomous vehicle reaches the closure point.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high quality digital maps are used to represent static objects and boundaries, then trajectory generation accuracy is improved, but the overhead for verifying and distributing map data to a fleet of autonomous vehicles increases substantially
Solution Approach 1:
The system enables autonomous vehicles to self-update their map data by autonomously detecting roadway closure events through perception systems and automatically generating map updates, eliminating the need for centralized verification and distribution infrastructure
Solution Approach 2:
The system implements a feedback mechanism where autonomous vehicles detect roadway closure events, generate map updates, and share these updates with other vehicles in real-time, creating a self-improving map data ecosystem across the fleet
2Adaptability or versatility
If perception systems are used to detect changed circumstances in the environment, then adaptability to dynamic changes is improved, but the detection range is limited and can be occluded by other vehicles
Solution Approach 1:
The system uses other autonomous vehicles as intermediary information carriers, where vehicles ahead detect roadway closure events and transmit this information to following vehicles, effectively extending detection range beyond individual sensor limitations
Solution Approach 2:
The system performs preliminary detection of roadway closure events by monitoring non-ego vehicle tracks and behavior patterns before the autonomous vehicle encounters the closure, providing advance warning and preparation time
3Extent of automation
If the autonomous vehicle relies on its own perception system to detect roadway closures, then system independence is improved, but the reaction time is insufficient due to limited detection range
Solution Approach 1:
The system performs preliminary detection of roadway closure events by analyzing non-ego vehicle tracks and behavior patterns at a distance, allowing the autonomous vehicle to identify potential closures before reaching the affected area and initiate appropriate responses in advance
Solution Approach 2:
The system uses tracks of non-ego vehicles as intermediary indicators to infer roadway closure events occurring beyond the autonomous vehicle's direct perception range, providing early warning while maintaining system independence
Data Source
AI summary
An autonomous vehicle control system and method may detect the tracks of non-ego vehicles in a roadway and use those tracks to detect roadway closure events ahead of an autonomous vehicle based at least in part on determinations that those non-ego vehicle tracks leave the roadway. By doing so, map anomalies may be identified and corrective actions may be automatically initiated prior to the autonomous vehicle arriving at the location where the non-ego vehicle tracks leave the roadway.