Road Segment Sinuous Driving Metric for Autonomous Route Safety
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating road segments with unpredictable dynamics, such as those involving human-driven vehicles that may engage in unpredictable maneuvers like overtaking, sudden braking, or parking. Existing technologies lack a comprehensive metric to assess driver behavior and driving patterns on specific road segments, which hinders the ability of autonomous vehicles to anticipate and avoid such unpredictable scenarios.
Innovation Solution
A method is developed to determine a sinuous driving metric for road segments, which assesses the sinuosity of driving based on probe data. This metric represents a measure of driver behavior and driving patterns, indicating how smooth or aggressive driving is on a particular road segment. The method involves processing probe data, which includes position information and trajectory data, to calculate lateral and forward movements, thereby determining the sinuous driving metric.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles avoid road segments with unpredictable dynamics, then safety is improved, but navigation flexibility and route options are reduced
Solution Approach 1:
The system performs preliminary analysis of road segment characteristics and driver behavior patterns before navigation decisions are made. By pre-calculating sinuous driving metrics and storing them in a database, the system can quickly query and use this information during route planning without real-time delays, thus maintaining both safety and navigation flexibility
Solution Approach 2:
The navigation system dynamically adjusts route selection based on real-time queries of sinuous driving metrics for different road segments. Rather than statically avoiding all complex roads, the system adaptively chooses paths that balance safety concerns with navigation efficiency, allowing flexible route optimization based on current traffic and road conditions
2Measurement precision
If a comprehensive metric for driver behavior is developed, then assessment accuracy is improved, but data processing complexity increases
Solution Approach 1:
The driver behavior assessment is segmented into distinct measurable components: lateral movement distance, forward movement distance, and sinuous driving metric. Each component is calculated independently from probe data points, allowing the complex assessment to be broken down into manageable computational steps that can be processed efficiently
Solution Approach 2:
A database serves as an intermediary layer between raw probe data collection and the navigation decision-making process. The sinuous driving metrics are pre-calculated and stored in this database, decoupling the complex data processing from real-time navigation queries and reducing overall system complexity
3Loss of information
If probe data is collected and processed to determine sinuous driving metrics, then driver behavior assessment is improved, but computational resources and time are consumed
Solution Approach 1:
The system performs preliminary calculation and storage of sinuous driving metrics from probe data in advance, before navigation decisions are required. By pre-processing the probe data and storing results in a database, the system avoids repeated real-time calculations during navigation queries, significantly reducing computational time when routes need to be planned
Solution Approach 2:
The system maintains continuous collection and processing of probe data from multiple vehicles, continuously updating the database of sinuous driving metrics. This continuous data accumulation improves the quality and reliability of driver behavior assessment over time, while the pre-processed nature of the data ensures that navigation queries remain computationally efficient
Data Source
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AI summary
A method is disclosed comprising: obtaining data associated with each road segment of at least one road segment, said data comprising: a representative of at least one link associated with the respective road segment; obtaining probe data associated with the respective road segment, the probe data comprising: at least one piece of position information; determining a sinuous driving metric, which is a value being indicative of a sinuosity of driving on the respective road segment based at least partially on the probe data and its allocation with respect to the respective road segment. It is further disclosed an according apparatus, computer program and system.