Route Speed Funnel Identification for Roadwork Zones
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Solution Overview
Problem
Autonomous vehicles face challenges in detecting roadwork zones due to limitations in current sensor capabilities, often misidentifying speed funnels for other road features like tunnels or transitions, which can lead to collisions and mishaps.
Innovation Solution
A method and system for generating route speed funnels by processing road sign observations from multiple vehicles to identify roadwork zones, involving the calculation of distances, heading differences, and sign value variations to filter and merge candidate speed funnels, thereby accurately indicating roadwork zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current sensors are used to detect road signs in autonomous vehicles, then the vehicle can capture road signs indicating roadwork zones, but the sensors are not capable of detecting certain road signs such as 'men at work' signs
Solution Approach 1:
The system aggregates data from multiple vehicles equipped with various sensors to create a comprehensive roadwork zone identification capability. Instead of relying on a single vehicle's limited sensor capabilities, the system pools observations from many vehicles to detect road signs that individual sensors might miss, achieving a form of multi-functional detection across the fleet.
2Reliability
If autonomous vehicles detect speed limit signs to identify roadwork zones, then they can report individual speed limit observations, but they may be misled by speed funnels that indicate other road features like tunnels or high curvature roads
Solution Approach 1:
The system merges speed limit observations from multiple vehicles to identify patterns that reliably indicate roadwork zones. By combining data from multiple sources and applying filtering criteria (threshold distance range, threshold heading range, threshold value range), the system distinguishes true roadwork zone indicators from false indicators like tunnels or curve warnings, improving identification reliability.
3Measurement precision
If autonomous vehicles rely on road signs at low speeds to identify roadwork zones, then they can detect the signs, but it is not possible to identify those signs at higher speeds
Solution Approach 1:
The system performs preliminary identification of roadwork zones by aggregating speed funnel data from multiple vehicles before an individual vehicle reaches the zone. This allows the system to identify roadwork zones at a distance and provide advance warning, giving drivers time to slow down and prepare, effectively compensating for the reduced detection capability at higher speeds.
4Productivity
If autonomous vehicles use speed funnels to identify roadwork zones, then they can detect speed limit signs along the driving path, but the speed funnel may indicate upcoming tunnels or high curvature roads instead of roadwork zones
Solution Approach 1:
The system uses feedback from multiple vehicle observations to validate whether a detected speed funnel truly indicates a roadwork zone. By analyzing whether multiple vehicles observe consistent speed limit patterns at similar locations and applying threshold-based filtering, the system confirms true roadwork zones while filtering out false positives from tunnels or curve warnings, improving identification accuracy.
Data Source
AI summary
A method, a system, and a computer program product may be provided for generating at least one route speed funnel for roadwork zone identification. The method may include generating a plurality of learned speed signs from a first plurality of road sign observations captured by a first plurality of vehicles, determining a plurality of candidate speed funnels from the plurality of learned speed signs, based on corresponding locations, corresponding headings, and corresponding sign values and generating at least one route speed funnel from the plurality of candidate speed funnels based on session identifiers associated with a second plurality of vehicles of the first plurality of vehicles and corresponding time stamps of the plurality of learned speed signs. Each of the plurality of candidate speed funnels comprises a different pair of learned speed signs selected from the plurality of learned speed signs.