Lane Assignment Using Dynamic Thresholds for Adjacent Vehicles
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Solution Overview
Problem
Current lane assignment systems for automated vehicles face challenges in accurately determining whether an adjacent vehicle is in the same lane or a different lane, leading to potential false-negative inputs and increased collision risks during automatic lane changes.
Innovation Solution
A lane assignment system utilizing a digital map and ranging sensors, such as radar, to determine the lateral variation and apply a dynamic threshold and low-pass filtering to accurately assess lane positions, preventing unsafe lane changes by distinguishing between vehicles in the same or adjacent lanes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a lane assignment system uses basic radar detection to determine adjacent vehicle positions, then the system complexity is low, but the measurement precision of lane position determination is insufficient leading to false-negative inputs
Solution Approach 1:
The patent combines multiple data sources including radar detections, digital map lane geometry data, and probabilistic road estimation algorithms into a unified lane assignment system. This integration merges heterogeneous data types to achieve accurate lane position determination while maintaining reasonable system complexity through coordinated processing of combined inputs.
Solution Approach 2:
The patent introduces a probabilistic road estimation algorithm as an intermediary layer between raw radar detections and lane position determination. This intermediary processes and filters radar data, associating vehicles with particular lanes through probabilistic reasoning, thereby improving measurement precision without directly increasing the complexity of individual system components.
2Reliability
If the system applies strict lane position thresholds to determine adjacent vehicle locations, then the reliability of lane change safety decisions is improved, but the measurement precision may be reduced due to threshold-induced false assignments
Solution Approach 1:
The patent employs dynamic threshold adjustment based on lateral variation analysis. Instead of using fixed thresholds, the system adapts threshold values according to the observed lateral movement patterns of detected vehicles. When lateral variation exceeds certain criteria, the system dynamically modifies assignment thresholds, thereby maintaining reliability while accommodating measurement uncertainties that would otherwise reduce precision.
Solution Approach 2:
The patent changes the parameter of threshold values from static to dynamic based on lateral variation conditions. By adjusting threshold parameters according to real-time lateral movement observations, the system optimizes both reliability and measurement precision, avoiding false-negative inputs while maintaining accurate lane position determination under varying operational conditions.
3Measurement precision
If the system uses probabilistic road estimation algorithms to associate vehicles with lanes, then the measurement precision of lane assignment is improved, but the loss of time for processing multiple data sources increases
Solution Approach 1:
The patent performs preliminary processing of radar detections and digital map data before applying probabilistic road estimation algorithms. By pre-processing and organizing raw data into structured formats, the system reduces the computational burden during lane assignment operations, thereby improving measurement precision while minimizing the time loss associated with processing multiple data sources.
4Adaptability or versatility
If the system detects vehicles with large lateral variation, then the adaptability to different driving behaviors is improved, but the measurement precision of lane position determination deteriorates due to ambiguous lane association
Solution Approach 1:
The patent dynamically adjusts lane assignment criteria based on observed lateral variation characteristics. For vehicles exhibiting large lateral movement, the system adapts by modifying association thresholds and using time-window-based analysis to distinguish between intentional lane changes and transient lateral movements. This dynamic adaptation maintains measurement precision while improving versatility in handling diverse driving behaviors.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively reduces false lane assignments and collision risks by accurately determining lane positions, ensuring safe operation of automated vehicles during lane changes.
Implementation Method 1
a ranging sensor 26, such as radar, that detects a lateral distance 28 to the other vehicle 16
Data Source
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AI summary
A lane assignment system (10) includes a digital-map (14), a ranging-sensor (26), and one or more controller-circuits (24). The digital-map (14) indicates a position (18) of a host-vehicle (12) traveling in a travel-lane (20) on a roadway (22). The ranging sensor detects a lateral-distance (28) to an other-vehicle (16) traveling on the roadway (22) proximate the host-vehicle (12). The one or more controller-circuits (24) are in communication with the digital-map (14) and the ranging-sensor (26). The one or more controller-circuits (24) determine a lateral-variation (30) of the lateral-distance (28), determine whether the lateral-variation (30) is greater than a dynamic-threshold (32), determine whether a second-lane (34) exists beyond a first-lane (36) based on the digital-map (14), determine that the other-vehicle (16) is traveling in the first-lane (36), and operate the host-vehicle (12) in accordance with the other-vehicle (16) traveling in the first-lane (36).