Multi-Sensor Lane Selection on Snowy and Rutted Roads
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Solution Overview
Problem
Existing Advanced Driver-Assistance Systems (ADAS) fail to determine the optimal driving lane under adverse road conditions such as snow, potholes, and rutted pavement, which can compromise sensor accuracy and road marking visibility.
Innovation Solution
A system and method utilizing a combination of sensors including LiDAR, vehicle speed and direction sensors, acceleration sensors, and microphone sensors to monitor road conditions in real-time, generate high-resolution 3D images of road surfaces, and determine the optimal driving lane. The system provides real-time feedback to the driver or automatically adjusts the vehicle's steering to navigate to the optimal lane.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor-based lane detection methods are used, then the system can detect lane markings under normal conditions, but sensor accuracy and road marking visibility are compromised under adverse road conditions such as snow, potholes, and rutted pavement
Solution Approach 1:
The patent transitions from 2D camera-based lane detection to 3D LiDAR-based spatial mapping. By creating three-dimensional point cloud representations of the road surface, the system can detect lane boundaries and road hazards in vertical and depth dimensions, not just horizontal planes. This dimensional enhancement allows the system to penetrate through adverse conditions like snow and potholes that obscure traditional 2D visual detection.
Solution Approach 2:
The system integrates multiple sensor types (LiDAR, cameras, radar) into a unified lane determination system that can operate across diverse road conditions. The LiDAR component provides universal detection capability for both lane markings and road surface hazards (potholes, snow, ruts), while the integration allows the system to switch between or combine sensor data sources depending on environmental conditions, making the overall system universally applicable across normal and adverse conditions.
2Measurement precision
If the system uses multiple sensors to improve detection accuracy under adverse conditions, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent divides the detection system into specialized functional modules: LiDAR for 3D spatial mapping and road surface analysis, cameras for 2D visual detection and color information, and radar for penetration through obstacles. Each sensor type is optimized for specific detection tasks, and the system processes each sensor's data through dedicated algorithms before integration. This segmentation allows complex multi-sensor functionality while maintaining manageable system architecture through modular design.
Solution Approach 2:
The system introduces an intermediary processing layer that fuses data from multiple sensors. Rather than directly combining raw sensor outputs, the patent uses intermediate representations such as 3D point clouds from LiDAR and 2D image features from cameras, which are then integrated through coordinate transformation and feature fusion algorithms. This intermediary processing stage simplifies the integration of heterogeneous sensor data by converting them into compatible formats and unified spatial references.
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
The system effectively determines and navigates to the optimal driving lane under adverse conditions, enhancing safety and comfort by minimizing exposure to hazardous road surfaces and improving sensor accuracy.
Implementation Method 1
A light-detection and ranging system (LiDAR) is a sensing method that uses pulsed laser light to measure variable distances to objects. Calculating the light's travel and wavelength, LiDAR creates 3D images of objects in a sensor's field of view.
Data Source
AI summary
A system and method for using a variety of sensors monitors road conditions in real time determines the best lane for vehicle travel. The system may automatically choose the best lane, or assist a driver in making that decision. Its sensors include LIDAR sensors, vehicle wheel-speed and yaw sensors, acceleration sensors, and microphone sensors.