In-Vehicle Lane Identification Using Sign and Marking Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicle navigation systems struggle to accurately identify and differentiate between exit-only lanes and shared exit lanes, leading to potential collisions and inefficient autonomous driving operations.
Innovation Solution
An in-vehicle lane identification system that detects lane markings and exit signs to determine the presence and location of exit-only and shared exit lanes by analyzing characteristics such as dash frequency, solid markings, arrow orientation, and text on signs, and communicates this information to autonomous driving systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing vehicle navigation systems are used, then basic navigation functionality is provided, but accurate identification and differentiation between exit-only lanes and shared exit lanes cannot be achieved
Solution Approach 1:
The system segments the lane identification task into multiple detection components: lane marking detection (detecting dashed vs. solid markings), exit sign detection (detecting arrows and text), and combination logic that integrates both sources of information to classify lanes as exit-only or shared exit lanes
Solution Approach 2:
The system introduces an intermediary classification layer that processes raw detection data from lane markings and exit signs, applies decision logic to determine lane type, and then communicates the classified lane information to the autonomous driving system for safe navigation decisions
2Measurement precision
If lane markings are detected to identify exit lanes, then lane identification is achieved, but differentiation between exit-only and shared exit lanes remains insufficient
Solution Approach 1:
The system merges two independent detection streams (lane marking detection and exit sign detection) into a unified lane classification system. By combining information from both sources and applying decision logic, the system achieves accurate differentiation between exit-only and shared exit lanes without requiring a single overly complex detection mechanism
Solution Approach 2:
The detection system is designed to perform multiple functions: detecting lane markings, detecting exit signs, analyzing arrow orientations, reading text on signs, and classifying lane types. This multi-functional approach allows a single system to handle the complete lane identification task without requiring separate specialized systems
3Reliability
If autonomous driving systems operate without precise lane identification, then basic autonomous driving is maintained, but safe navigation through complex lane configurations cannot be achieved
Solution Approach 1:
The system performs preliminary detection and classification of lane types (exit-only vs. shared exit) in advance, before the vehicle reaches the exit area. This early identification allows the autonomous driving system to prepare appropriate navigation decisions and allows drivers to anticipate upcoming lane configurations, reducing reaction time when decisions are required
Data Source
AI summary
An in-vehicle system for identifying exit-only lanes and shared exit lanes on a roadway having a first camera for obtaining image data regarding lane markings on the roadway, a second camera for obtaining image data regarding exit signs, a lane marking detection module for detecting lane markings on the roadway, an exit sign detection module for detecting exit signs, and an exit sign analyzer for detecting arrows on the detected exit signs. The in-vehicle system categorizes detected lane markings as one of standard frequency dashed lane markings, high frequency dashed lane markings, and solid lane markings, and identifies an exit-only lane and a shared exit lane in response to the categorizations of the detected lane markings and characteristics of the detected arrows.