Lane-Specific Speed Profiles via Probe Data Perpendicular Distance
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Solution Overview
Problem
Current systems for adaptive cruise control, autonomous navigation, and platooning lack accurate and granular speed information for vehicles, which hinders the ability to provide a human-like driving experience and efficient energy use.
Innovation Solution
A method to determine lane-specific speed profiles by analyzing perpendicular distances between probe data points and map data, allowing for the creation of detailed speed profiles that can be used by autonomous and assisted-driving vehicles to adjust their speed accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lane-specific speed profiles are determined using perpendicular distances from probe data points, then speed information accuracy is improved, but data processing complexity increases
Solution Approach 1:
The method segments the road network into individual links and further into lanes, determining speed profiles for each lane separately. Probe data points are assigned to specific lanes based on their perpendicular distance to the link centerline, enabling lane-specific speed analysis that improves accuracy while maintaining manageable processing complexity through systematic division of the problem space.
Solution Approach 2:
The perpendicular distance serves as an intermediary parameter that connects probe data points to specific lanes. By calculating the perpendicular distance from each probe point to the link centerline and comparing it against lane width thresholds, the system efficiently assigns data points to appropriate lanes without requiring complex spatial algorithms, thus improving speed information accuracy while keeping processing relatively simple.
2Speed
If continuous real-time sensor input is used for speed adjustment, then responsiveness to traffic conditions is improved, but energy consumption increases
Solution Approach 1:
The system performs preliminary action by pre-determining lane-specific speed profiles using historical probe data from multiple vehicles. These pre-computed profiles are stored and can be directly applied when a vehicle enters a particular lane, providing responsive speed adjustments without requiring continuous real-time sensor processing, thus reducing energy consumption while maintaining adaptability to traffic conditions.
Solution Approach 2:
Instead of continuously sensing and processing real-time data, the system creates copies of speed profile information from historical probe data. These copied profiles represent typical speed patterns for each lane and can be reused multiple times without additional sensing overhead, enabling responsive speed control while significantly reducing energy consumption compared to continuous real-time monitoring.
Data Source
AI summary
A method is presented which includes obtaining or holding available map data representing a travel network of road links and determining at least one lane-specific speed profile for a specific lane of at least one selected link of the plurality of links based, at least in part, on perpendicular distances between the selected link as represented by map data and positions represented by a plurality of data points.