Vehicle Road Bend Detection Using Camera Light Gradients
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Solution Overview
Problem
Current vehicle lighting systems struggle to anticipate bends in the road effectively, leading to inadequate illumination and reduced safety, especially on roads with many bends, due to delays in headlight orientation changes and reliance on imprecise navigation systems or absent road markings.
Innovation Solution
A method using a camera and image processing unit to detect road edges and analyze light gradients, combined with a neural network to determine bend angles and orientations, allowing for precise anticipation of road geometry and adaptive headlight adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If steering wheel angle sensor information is used to control headlight orientation, then the device complexity is reduced, but the measurement precision of road geometry is insufficient and the reliability is reduced due to driver behavior interference
Solution Approach 1:
The patent introduces an intermediary system (camera + image processing unit) between the vehicle and the road geometry detection. Instead of directly using steering angle or navigation data, the system captures images of the road scene and processes them to detect road edges and bends, providing more reliable and precise road geometry information that is not affected by driver behavior or navigation inaccuracies.
2Ease of operation
If steering wheel angle sensor is used for headlight orientation, then the ease of operation is improved, but the loss of time occurs due to delay in detecting bend entry and headlight response
Solution Approach 1:
The system performs preliminary detection of road bends by continuously analyzing road edge images before the vehicle actually enters the bend. By detecting the bend geometry in advance and calculating the required headlight orientation提前, the system enables the headlights to be oriented correctly as the vehicle approaches the bend, eliminating the time delay associated with reactive steering angle-based systems.
3Loss of time
If navigation system information is used for headlight orientation, then the anticipation of road geometry is improved, but the measurement precision is reduced due to imprecise cartography and GPS data
Solution Approach 1:
The system performs self-service by using the vehicle's own camera and image processing capabilities to detect and measure road geometry directly from the road scene. This eliminates dependence on external navigation systems and their imprecise cartography and GPS data. The vehicle essentially measures the road geometry itself through image analysis of road edges, providing accurate real-time bend information.
4Measurement precision
If road marking detection is used for headlight orientation, then the measurement precision of road geometry is improved, but the adaptability is reduced due to absence of markings in many areas
Solution Approach 1:
The system achieves universality by detecting road geometry through road edges rather than relying on specific road markings. By identifying the contrast between the road surface and its edges in captured images, the system can detect bends and orient headlights on any road type regardless of whether it has markings, gravel shoulders, grass verges, or other edge features. This makes the system adaptable to all road conditions.
Data Source
AI summary
The invention concerns a method of detecting from a vehicle a bend in a road comprising a surface and road edges, comprising the following operations:taking an image of a road scene unfolding in front of the vehicle and at least partly illuminated by the vehicle,determining, for each pixel in the image a light decrease gradient,analysing these decrease gradients and determining an image of the road edges,mathematically discriminating the gradients from the image of the road edges,analyzing this discrimination and determining edges, and an angle of the bend.The invention also concerns a system for implementing the method of the invention comprising a camera mounted in the vehicle, an image processing unit and a neural network.