Camera Road Surface Modeling for Reflective Hazard Detection
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Solution Overview
Problem
Current vehicle environment modeling techniques using cameras face challenges in accurately detecting road surfaces and obstacles, especially in varying weather conditions and reflective surfaces, due to noise interference and the need for real-time hazard detection, which complicates autonomous driving systems.
Innovation Solution
The implementation of a camera-based system utilizing a deep neural network (DNN) to compute gamma values directly from image sequences, incorporating sensor motion and epipole information, which helps in accurately modeling road surfaces and distinguishing reflective areas like puddles, enabling better vehicle control and obstacle avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If camera-based systems are used to detect road surfaces and obstacles, then object detection and recognition capabilities are improved, but noise interference from shadows, lights, and reflective surfaces degrades measurement precision
Solution Approach 1:
The patent segments the image processing task into multiple specialized neural networks: one network detects road surfaces while another detects puddles and reflective areas. This segmentation allows each network to specialize in specific detection tasks, improving overall precision by handling different surface types and lighting conditions separately rather than attempting unified detection.
Solution Approach 2:
The patent introduces an intermediary processing layer that analyzes pixel intensity variations and motion patterns between sequential images. This intermediary analysis acts as a filter to distinguish between actual road surface features and noise from shadows or reflective surfaces, thereby improving measurement precision before final detection decisions are made.
2Reliability
If complex processing algorithms are implemented to handle varying weather and road conditions, then detection reliability is improved, but hardware burden and device complexity increase
Solution Approach 1:
The patent replaces complex mechanical or computational processing systems with a dedicated neural network architecture specifically designed for road surface detection. This specialized neural network hardware implementation maintains high detection reliability across varying weather and road conditions while reducing overall device complexity compared to general-purpose complex processing algorithms.
Solution Approach 2:
The patent designs a universal neural network system that handles multiple detection tasks (road surfaces, puddles, obstacles, shadows) within a single integrated architecture. This multi-functional approach improves reliability across diverse conditions while avoiding the need for separate specialized hardware for each detection type, thereby reducing device complexity.
3Speed
If real-time hazard detection is implemented to enable autonomous driving, then response speed is improved, but processing complexity and hardware requirements increase
Solution Approach 1:
The patent performs preliminary processing by pre-training specialized neural networks on extensive datasets of road surfaces, puddles, and obstacles under various weather conditions. This preliminary training enables the networks to perform real-time detection with simplified processing during actual autonomous driving operation, achieving fast hazard detection while keeping runtime processing complexity manageable.
Data Source
AI summary
System and techniques for vehicle environment modeling with a camera are described herein. A time-ordered sequence of images representative of a road surface may be obtained. An image from this sequence is a current image. A data set may then be provided to an artificial neural network (ANN) to produce a three-dimensional structure of a scene. Here, the data set includes a portion of the sequence of images that includes the current image, motion of the sensor from which the images were obtained, and an epipole. The road surface is then modeled using the three-dimensional structure of the scene.