Autonomous Vehicle Lane Closure Detection via Lateral Distance Analysis
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting lane closures and shifts, particularly in construction zones where obstacles like cones are used to divert traffic, as existing systems struggle to differentiate between lane blockages and shifts.
Innovation Solution
An autonomous vehicle system that uses sensors like LIDAR, RADAR, or cameras to determine the presence and position of obstacles within a lane, calculates lateral distances, and compares these to pre-determined thresholds to differentiate between lane closures and shifts, allowing for safe navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing sensor systems are used to detect obstacles, then basic obstacle detection is achieved, but the system cannot accurately differentiate between lane closures and lane shifts
Solution Approach 1:
The patent introduces a new dimension of analysis by calculating lateral distance from the lane center to obstacles, transforming the detection from simple presence/absence to spatial relationship analysis. This dimensional addition enables differentiation between lane closures (obstacles near center) and lane shifts (obstacles at edges)
Solution Approach 2:
The system changes the parameter being measured from mere obstacle presence to lateral distance from lane center. By comparing this distance parameter against threshold values, the system can distinguish between different lane status conditions (closure vs. shift) that would otherwise be indistinguishable
2Reliability
If the autonomous vehicle uses simple obstacle presence detection, then the system is computationally efficient, but it cannot provide robust navigation control in construction zones
Solution Approach 1:
The detection system is segmented into distinct functional components: obstacle presence detection, lateral distance calculation, threshold comparison, and control instruction generation. This modular approach increases reliability through systematic processing while managing complexity through clear separation of functions
3Reliability
If the vehicle responds to all detected obstacles by stopping, then safety is maximized, but productivity and efficiency are reduced
Solution Approach 1:
The system uses parameter-based decision making by comparing lateral distance against thresholds to determine appropriate responses. This allows differentiated control actions (continue, slow down, stop) based on the specific geometric parameters of the obstacle configuration, balancing safety with productivity
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 efficiently, accurately, and robustly detect lane closures and shifts, ensuring safe operation through the provision of precise control instructions based on sensor data analysis.
Implementation Method 1
uses sensors like LIDAR, RADAR, or cameras to determine the presence and position of obstacles within a lane
Implementation Method 2
uses sensors like LIDAR, RADAR, or cameras to determine the presence and position of obstacles within a lane
Data Source
AI summary
In an example implementation, an autonomous vehicle is configured to detect closures and lane shifts in a lane of travel. The vehicle is configured to operate in an autonomous mode and determine a presence of an obstacle substantially positioned in a lane of travel of the vehicle using a sensor. The lane of travel has a first side, a second side, and a center, and the obstacle is substantially positioned on the first side. The autonomous vehicle includes a computer system. The computer system determines a lateral distance between the obstacle and the center, compares the lateral distance to a pre-determined threshold, and provides instructions to control the autonomous vehicle based on the comparison.