Road Work Detection Using Probe Vehicle Sensor Data
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Solution Overview
Problem
Conventional map data often lacks temporal relevance and accuracy in representing road work events, leading to inefficiencies in navigation and autonomous vehicle control due to the dynamic and frequently changing nature of road work conditions.
Innovation Solution
A method and apparatus that utilize vehicle sensor data to identify road work by receiving probe data from multiple sources, detecting indicators of road work areas, and determining the probability of road work presence along road segments, providing real-time and accurate information for navigational assistance and autonomous vehicle control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional map data is used to represent road work events, then the system is simple to operate, but the temporal relevance and accuracy of road work information deteriorates
Solution Approach 1:
The patent combines data from multiple sources including municipal publications, sensor data from probe vehicles, and map data into a unified road work detection system. This merging of diverse data sources improves the accuracy and temporal relevance of road work information while distributing the complexity across multiple integrated components rather than relying on a single complex system
Solution Approach 2:
The system uses sensor data from probe vehicles that are already traveling on the road network to automatically detect road work conditions. This self-service approach leverages existing vehicle sensors and their natural movement through the network, improving detection accuracy without requiring a separate dedicated detection infrastructure
2Reliability
If municipal road work events are published with tentative schedules, then the information is easy to generate, but the temporal relevance deteriorates due to events not accounting for various impacts on scheduling
Solution Approach 1:
The system continuously collects sensor data from probe vehicles and uses this feedback to update and refine road work detection in real-time. This feedback mechanism allows the system to detect actual road work conditions as they occur and adjust information accordingly, improving temporal relevance while maintaining efficient automated processing of sensor inputs
Solution Approach 2:
The system transitions from static, pre-published road work schedules to dynamic, real-time detection using sensor data from moving probe vehicles. This dynamic approach allows the system to adapt to changing road work conditions and scheduling impacts automatically, improving reliability while maintaining productivity through automated sensor processing
3Measurement precision
If map data refresh rate is increased to improve temporal relevance, then the accuracy of road work information improves, but the loss of time and computational resources increases
Solution Approach 1:
The system performs preliminary detection of road work conditions using sensor data from probe vehicles as they naturally traverse the network. By detecting road work events in advance through continuous sensor monitoring and processing them before they affect navigation decisions, the system achieves high temporal accuracy without requiring frequent full-map refreshes, thus reducing time loss
Solution Approach 2:
Instead of continuous full-map refreshing, the system uses periodic sensor data collection from probe vehicles that naturally pass through road work areas. This periodic sampling approach maintains temporal accuracy by capturing road work events when vehicles encounter them, while minimizing computational overhead by processing only relevant sensor data from vehicles in proximity to detected events
Data Source
AI summary
Embodiments described herein may provide a method for using vehicle sensor data to identify where road work exists within a road network. Methods may include: receiving probe data and sensor data from a plurality of probe apparatuses traveling along a sequence of road segments; identifying, from the sensor data, one or more indicators of a beginning of a road work area; identifying, from the sensor data, binary indicators of the presence of road work or a lack of presence of road work along the sequence of road segments; and determining, based on the one or more indicators of a beginning of a road work area and the binary indicators of the presence of road work or the lack of road work along the sequence of road segments, a probability of road work occurring along one or more road segments of the sequence of road segments.