LIDAR Construction Zone Sign Detection for Autonomous Vehicles
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Solution Overview
Problem
Autonomous vehicles face challenges in detecting construction zones, as changes due to construction are not always reflected in their maps, which can lead to unsafe navigation.
Innovation Solution
A method using a LIDAR sensor to create a 3D point cloud of the vehicle's vicinity, identifying points at a threshold height above the road surface, estimating shapes, and determining the likelihood of construction zone signs based on intensity values and shapes, to modify the vehicle's control strategy for safe navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles rely on pre-stored map data for navigation, then the navigation system is simple and efficient, but the system cannot detect construction zones that are not reflected in the maps
Solution Approach 1:
The patent introduces LIDAR technology as an intermediary sensor system that bridges the gap between outdated map data and real-world construction zones. The LIDAR sensor actively scans the environment and detects construction zone signs by analyzing reflected light patterns, serving as a mediator that provides real-time detection capabilities without completely replacing the map-based navigation system.
Solution Approach 2:
The patent segments the detection task into specific geometric patterns (such as diamond shapes, rectangles, and triangles) that correspond to construction zone signs. By dividing the complex detection problem into recognizable shape categories with specific orientation and intensity characteristics, the system can efficiently identify construction zones without requiring complete re-mapping of all areas.
2Measurement precision
If the vehicle uses LIDAR to detect construction zone signs in real-time, then detection accuracy improves, but computational load and processing time increase
Solution Approach 1:
The patent applies local quality analysis by focusing computational resources on specific regions of interest within the LIDAR point cloud data. Instead of processing the entire 360-degree environment uniformly, the system concentrates analysis on areas where construction zone signs are likely to appear (such as road edges and intersections), reducing overall processing time while maintaining detection precision.
Solution Approach 2:
The patent implements partial action by detecting only the essential geometric features (shape, orientation, intensity) of construction zone signs rather than attempting to fully reconstruct and analyze every object in the environment. This selective detection approach achieves sufficient precision for safety-critical applications without the computational burden of complete environmental understanding.
3Reliability
If the vehicle modifies control strategy based on detected construction zones, then navigation safety improves, but the control system becomes more complex
Solution Approach 1:
The patent implements preliminary action by pre-defining a set of control strategies corresponding to different construction zone scenarios (such as lane closure, speed reduction, or alternative routing). When construction zone signs are detected, the system matches the detected pattern against these pre-defined strategies and executes the appropriate one, avoiding the need for complex real-time decision-making algorithms while maintaining high navigation safety.
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
Enables autonomous vehicles to safely detect and navigate through construction zones by accurately identifying signs and modifying driving behavior, even when map data is outdated or incomplete.
Implementation Method 1
LIDAR-based information comprising (i) a three-dimensional (3D) point cloud of a vicinity of a road on which the vehicle is travelling, and the 3D point cloud may comprise points corresponding to light emitted from the LIDAR and reflected from one or more objects in the vicinity of the road
Data Source
AI summary
Methods and systems for construction zone sign detection are described. A computing device may be configured to receive a 3D point cloud of a vicinity of a road on which a vehicle is travelling. The 3D point cloud may include points corresponding to light reflected from objects in the vicinity of the road. The computing device may be configured to determine a set of points representing an area at a given height from a surface of the road, and estimate a shape associated with the set of points. Further, the computing device may be configured to determine a likelihood that the set of points represents a construction zone sign, based on the estimated shape. Based on the likelihood, the computing device may be configured to modify a control strategy associated with a driving behavior of the vehicle; and control the vehicle based on the modified control strategy.