Roadwork Zone Identification via Speed Funnel Analysis
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Solution Overview
Problem
Current navigation technologies face challenges in accurately identifying and communicating roadwork zones to autonomous vehicles, particularly at high speeds, leading to potential collisions and increased travel time due to unclear pictorial and iconic signs.
Innovation Solution
A system and method for generating roadwork extension data by obtaining speed funnel data, determining candidate roadwork links, and using vehicular trajectory data to identify qualified roadwork links, allowing for advanced planning of drive mode and route through roadwork zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If roadwork zones are indicated using road signs and lane markings, then drivers can be informed of roadwork zones, but identification becomes difficult at high speeds and signs may not be clearly visible
Solution Approach 1:
The system performs preliminary identification of roadwork zones by analyzing speed funnel patterns and vehicular trajectory data before the vehicle reaches the zone. This allows the autonomous vehicle to be notified in advance and transition to manual mode or plan alternative routes, resolving the contradiction by providing early warning rather than relying on visible signs at high speeds
2Reliability
If autonomous vehicles rely on visual road signs for navigation, then the system remains simple, but identification reliability decreases at high speeds or poor visibility conditions
Solution Approach 1:
The system introduces an intermediary data processing layer that analyzes speed funnel data and vehicular trajectory patterns to indirectly identify roadwork zones. Instead of directly observing road signs, the system uses behavioral data from multiple vehicles to infer the presence of roadwork zones, improving reliability without requiring complex visual recognition systems
Solution Approach 2:
The system collects and analyzes vehicular trajectory data from multiple sources, using feedback loops to continuously refine roadwork zone identification accuracy. By processing speed funnel data and comparing observed vehicle behavior against expected patterns, the system improves detection reliability through iterative learning
3Loss of time
If drivers are notified of roadwork zones at the last moment, then navigation response time is reduced, but drivers and passengers experience wastage of time and energy due to sudden detours or mode switching
Solution Approach 1:
The system provides preliminary notification of roadwork zones by detecting speed funnel patterns and analyzing vehicular trajectory data before the autonomous vehicle approaches the zone. This early warning allows the navigation system to plan alternative routes or prepare for mode transition in advance, reducing both travel time loss and information loss by providing timely alerts
Data Source
AI summary
Various aspects of a system, a method, and a computer program product for generation of roadwork extension data of a roadwork zone are disclosed herein. In accordance with an embodiment, the system includes a memory and a processor. The processor may be configured to obtain speed funnel data of one or more speed funnels. The processor may be configured to determine a plurality of candidate roadwork links, based on the speed funnel data. The processor may be configured to obtain vehicular trajectory data corresponding to the plurality of candidate roadwork links. The processor may be further configured to determine at least one qualified roadwork link from the plurality of candidate roadwork links, based on the vehicular trajectory data and speed threshold data of the plurality of candidate roadwork links. The processor may be further configured to generate the roadwork extension data based on the at least one qualified roadwork link.