Vehicular Radar-Camera Lane Prediction During Camera Dropout
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Solution Overview
Problem
Existing vehicle sensing systems that combine radar and camera data struggle to accurately determine lane markings when camera data is interrupted by environmental conditions, leading to potential errors in vehicle control.
Innovation Solution
A vehicular sensing system that utilizes a combination of radar sensors and cameras, where the electronic control unit processes both image and radar data to determine road edges and lane markings. When camera data is unavailable, the system predicts lane marking locations based on radar data, allowing for continuous vehicle control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system relies on camera data to determine lane markings, then measurement precision is improved, but reliability deteriorates when environmental conditions interrupt camera data
Solution Approach 1:
The system merges camera-based lane marking detection with radar-based road edge detection to create a hybrid system. The ECU processes both camera image data and radar data simultaneously, allowing the system to maintain reliability by switching to or supplementing with radar data when camera data is interrupted by environmental conditions.
Solution Approach 2:
The ECU acts as an intermediary that processes and integrates data from both camera and radar sensors. It determines lane markings from camera data when available, and switches to or supplements with radar-based road edge detection when camera data is interrupted, ensuring continuous and reliable operation.
2Device complexity
If the system uses only camera data for lane marking detection, then device complexity is reduced, but reliability deteriorates under adverse environmental conditions
Solution Approach 1:
The system combines camera and radar sensors into an integrated sensing system. The ECU processes both camera image data and radar data to determine lane markings and road edges, creating a more reliable system that can operate under various environmental conditions while managing the complexity through unified data processing.
3Reliability
If the system switches to radar data when camera data is unavailable, then reliability is improved, but measurement precision may deteriorate
Solution Approach 1:
The ECU serves as an intermediary that intelligently processes both camera and radar data. When camera data is available, it uses camera-based lane marking detection for high precision. When camera data is interrupted, it switches to or supplements with radar-based road edge detection to maintain continuous operation, managing the trade-off between precision and reliability.
Solution Approach 2:
The system dynamically adjusts its data processing strategy based on environmental conditions and sensor availability. The ECU can switch between camera-based and radar-based detection methods, or combine both, depending on which sensor provides reliable data at any given moment, optimizing both precision and reliability in real-time.
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 predicts lane markings even when camera data is interrupted, ensuring accurate vehicle control and maintaining safety by utilizing radar data to supplement camera inputs.
Implementation Method 1
a radar sensor disposed at the vehicle that senses exterior of the vehicle. The radar sensor captures radar data
Implementation Method 2
A vehicular sensing system includes a camera disposed at a vehicle equipped with the vehicular sensing system that views exterior of the vehicle. The camera captures image data
Data Source
AI summary
A vehicular sensing system includes a camera and a radar sensor disposed at a vehicle. The system, responsive to processing of radar data captured by the radar sensor, determines an edge of a road the vehicle is traveling along. Responsive to processing of image data captured by the camera, location of a lane marking of the road is determined. As the vehicle travels along the road, the system, responsive to failing to determine the location of the lane marking of the road via processing of the image data captured by the camera, predicts the location of the lane marking of the road based on the radar data. The vehicle is controlled based in part on the predicted location of the lane marking of the road.