Autonomous Lane Merge Navigation Using Lane Type Classification
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating lane changes and merges due to the lack of effective systems for identifying lane types and adjusting navigation paths accordingly, which affects safe and accurate travel.
Innovation Solution
A computer-implemented method and system using cameras and processing devices to analyze images of the environment, identify target vehicles and lane marks, detect lane characteristics, determine lane types, and adjust navigational actions such as steering, braking, or acceleration based on the determined lane types and vehicle characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use basic lane detection without lane type classification, then the system complexity is low, but the navigation safety and accuracy deteriorate during lane merges and splits
Solution Approach 1:
The patent segments lane detection into multiple classification levels: basic lane detection, lane mark type identification (solid, dashed, merge, split), and navigational implication analysis. This segmentation allows the system to process lane information in hierarchical stages, improving navigation safety through comprehensive classification while managing system complexity by breaking down the overall task into manageable components.
Solution Approach 2:
The system performs preliminary classification of lane marks before making navigation decisions. By pre-identifying lane types (merge, split, solid, dashed) and predicting potential vehicle maneuvers based on lane configuration, the system prepares navigational strategies in advance, improving safety and accuracy during dynamic situations like lane merges and splits.
2Measurement precision
If autonomous vehicles perform comprehensive lane mark analysis to determine lane types, then navigation accuracy improves, but the processing time increases
Solution Approach 1:
The patent segments lane mark analysis into distinct processing stages: lane mark detection, type classification (solid, dashed, merge, split), and navigational decision-making. This segmentation enables parallel processing of different lane mark characteristics, maintaining high identification accuracy while reducing overall processing time by avoiding sequential analysis of all features.
Solution Approach 2:
The system performs partial analysis by focusing on the most critical lane mark characteristics relevant to current navigation context. Rather than analyzing all possible lane mark features equally, the system prioritizes identification of lane type and merge/split configurations, achieving sufficient accuracy for safe navigation while minimizing processing time by omitting less critical analysis.
3Adaptability or versatility
If autonomous vehicles do not classify lane marks by type, then the computational load is low, but the ability to respond to dynamic road conditions deteriorates
Solution Approach 1:
The patent segments lane mark classification into specific categories (solid, dashed, merge, split) with distinct navigational implications. This segmentation enables the system to adapt to different road conditions by applying pre-defined response strategies for each lane type, improving versatility while managing processing complexity through structured classification rather than unstructured analysis.
Solution Approach 2:
The system changes processing parameters based on detected lane mark types. When solid lanes are detected, the system adopts more conservative navigation parameters; when merge or split lanes are identified, it adjusts speed and positioning parameters accordingly. This parameter adaptation improves response to dynamic conditions while keeping processing complexity manageable through rule-based adjustments rather than complex real-time optimization.
Data Source
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AI summary
Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.