ADAS Merge Lane Control via Curve Fitting and Speed Profiling
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Solution Overview
Problem
Existing advanced driver assistance systems (ADAS) face challenges in controlling vehicles when merging lanes, as they struggle to recognize and manage lane width changes and interactions with other vehicles, leading to difficulties in avoiding collisions.
Innovation Solution
A driver assistance device and method that uses image and map information to recognize merge lanes, divide the lane into sections, and adjust driving speed based on detected vehicle locations and speeds, generating a driving route through curve fitting and speed profiling to ensure safe merging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the autonomous driving control device recognizes and controls only one lane, then the control simplicity is maintained, but the ability to avoid collision with other vehicles in merge lanes is compromised
Solution Approach 1:
The patent divides the lane recognition and control process into multiple segments: identifying the vehicle's current lane, detecting merge lanes using map information, recognizing other vehicles in adjacent lanes, and generating separate control strategies for each segment. This segmentation allows the system to handle complex merge lane scenarios without overwhelming complexity, while maintaining collision avoidance capabilities through targeted control in each segment.
Solution Approach 2:
The patent applies preliminary action by using map information to identify merge lanes before the vehicle actually enters them. The system proactively prepares control strategies for merge lane scenarios in advance, recognizing potential collision risks and planning avoidance maneuvers before other vehicles become immediate threats, thereby improving reliability without requiring complex real-time reaction to all possible scenarios.
2Measurement precision
If the system uses both map information and image information to recognize merge lanes, then the lane recognition accuracy is improved, but the information processing complexity increases
Solution Approach 1:
The patent merges map information (providing prior knowledge of merge lane locations and geometries) with image information (providing real-time visual confirmation of current lane status and other vehicles). This combination allows the system to achieve high recognition accuracy by cross-validating data from both sources, while the integrated processing architecture manages complexity through unified information fusion rather than separate parallel processing systems.
Solution Approach 2:
The patent introduces an intermediary processing layer that reconciles map information with image information. This intermediary component matches visual lane detections with pre-stored merge lane map data, filtering and validating information from both sources before generating control decisions. This mediator approach improves accuracy by ensuring consistency between map and visual data while managing processing complexity through a structured intermediate processing stage.
3Reliability
If the system adjusts driving speed based on other vehicles in merge lanes, then the collision avoidance capability is improved, but the driving control complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors the positions and speeds of other vehicles in merge lanes, compares this information with the vehicle's own trajectory and speed, and adjusts control actions accordingly. This feedback loop enables dynamic speed adjustment to maintain safe distances and avoid collisions, while the structured feedback architecture manages control complexity through systematic information processing rather than ad-hoc decision making.
Data Source
AI summary
A control method of a vehicle may include generating navigation information based on destination information and current location information; determining whether a lane to be driven is a merge lane based on the generated navigation information, map information, and the current location information; recognizing a lane in an image acquired by an imaging device; recognizing a driving first lane based on the recognized location information of the lane; dividing a certain area including the merge lane into an entry section, a merge section, and a stabilization section when the first lane converges with a second lane; generating a driving route by performing curve fitting for route points in the entry section and the merge section; and controlling autonomous driving based on the generated driving route.