Radar-Camera Fusion for Road Elevation-Based Vehicle Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating effectively due to the vast amounts of data they need to process and store, particularly in interpreting visual information and maintaining accurate location within roadways, which can lead to inefficiencies and limitations in navigation.
Innovation Solution
A system comprising a processor with circuitry and memory that receives images from a camera, uses RADAR indicators to determine the range and elevation of road surfaces, and initiates navigational actions based on this data, allowing for efficient identification and response to objects in the vehicle's environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used for navigation, then the vehicle can navigate using existing map data, but the sheer volume of data needed to store and update the map poses daunting challenges
Solution Approach 1:
The patent extracts only the essential navigation elements (road surface elevation, lane boundaries, intersections) from the environment using sensor fusion, rather than storing and processing complete traditional maps. This selective extraction reduces data storage requirements while maintaining navigation accuracy.
Solution Approach 2:
The system uses sensor fusion as an intermediary between raw environmental data and navigation decisions. By combining camera, LIDAR, and radar data to create a simplified representation of road geometry and features, the system avoids the need to store large volumes of traditional map data while achieving reliable navigation.
2Reliability
If vast volumes of visual and sensor data are collected and analyzed, then the vehicle can make informed navigation decisions, but this poses a multitude of design challenges and can limit or adversely affect autonomous navigation
Solution Approach 1:
The system extracts only the critical information needed for navigation decisions from vast volumes of sensor data. By focusing on essential elements like road elevation, lane markings, and key obstacles rather than processing all collected data, the system reduces processing complexity while maintaining decision accuracy.
Solution Approach 2:
The patent segments the data processing task into distinct modules: sensor data acquisition, feature extraction, road geometry determination, and navigation decision-making. This segmentation allows each module to handle specific aspects of data processing efficiently, reducing overall system complexity.
3Measurement precision
If the vehicle processes and interprets visual information from cameras and other sensors, then it can identify location and navigate safely, but the sheer quantity of data to analyze, access, and store poses challenges
Solution Approach 1:
The system employs sensor fusion as an intermediary that combines data from multiple sources (camera, LIDAR, radar) to directly determine road surface elevation and vehicle location. This intermediary process extracts precise location information without requiring analysis of the full volume of raw sensor data, reducing data processing requirements while maintaining measurement precision.
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
This solution enables autonomous vehicles to navigate more accurately and efficiently by processing and responding to visual and sensor data, reducing the need for extensive data storage and improving obstacle detection and route management.
Implementation Method 1
receive from a RADAR system at least one indicator of a range between the host vehicle and the target vehicle
Implementation Method 2
receive a plurality of images acquired by a camera onboard the host vehicle; identify a representation of a target vehicle in at least one of the plurality of images
Data Source
AI summary
Systems and methods for navigating a host vehicle based on RADAR-camera fusion are disclosed. In one implementation, a system includes a processor configured to receive images acquired by a camera onboard the host vehicle; identify a representation of a target vehicle in one of the images; receive from a RADAR system an indicator of a range between the host vehicle and the target vehicle; based on analysis of the images, identify in the image a ground intersection point associated with the target vehicle and a road surface; and determine an elevation value for the road surface based on the indicator of the range between the host vehicle and the target vehicle, the determined ground intersection point, and an angle of inclination between an optical axis of the camera and a ray directed toward a location of the ground intersection point.