Optical Distance Measurement Using Radar-Lidar Dynamic Separation
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Solution Overview
Problem
Lidar measurements struggle to differentiate between dynamic and static objects in complex driving scenarios, leading to challenging data association and degraded performance in dynamic object tracking and static object representation.
Innovation Solution
Combining Radar and Lidar measurements by creating a grid map from Radar data to extract dynamic state information and associating Lidar point clouds with this grid map, allowing for the classification of measurements as static or dynamic based on velocity information from Radar data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Lidar measurements are used for high-resolution environment perception, then measurement precision is improved, but data association becomes challenging and tracking performance degrades due to inability to distinguish dynamic objects from static objects
Solution Approach 1:
The patent combines Lidar measurements with Radar measurements to create a fused measurement system. The Radar provides velocity information that is merged with the high-resolution spatial data from Lidar, enabling reliable distinction between dynamic and static objects while maintaining measurement precision.
Solution Approach 2:
The patent introduces velocity information from Radar measurements as an intermediary parameter to facilitate data association. This intermediary information serves as a key discriminator to associate Lidar points with corresponding objects, resolving the ambiguity between static and dynamic objects.
2Device complexity
If Lidar measurements alone are used for object detection, then device complexity is reduced, but velocity information cannot be obtained and dynamic object tracking performance degrades
Solution Approach 1:
The patent merges Lidar and Radar measurement systems to simultaneously obtain spatial information from Lidar and velocity information from Radar. This combination ensures that velocity information is captured without significantly increasing device complexity, as both sensors work in complementary fashion.
Solution Approach 2:
The patent creates a multi-functional measurement system where the combined Lidar-Radar setup serves multiple functions: spatial mapping from Lidar, velocity measurement from Radar, and integrated object classification. This universal system handles both static and dynamic object detection effectively.
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 approach significantly improves data association by accurately distinguishing between reflections from static and dynamic objects, enhancing the performance of optical distance measurements and reducing ambiguity in complex driving scenarios.
Implementation Method 1
Radar measurements are carried out. In particular, the term 'Radar measurements' refers to measurements using radio waves to determine the distance to objects
Implementation Method 2
The method particularly comprises sending out radio waves which are reflected on objects in a measurement area
Implementation Method 3
Lidar measurements are based on sending out measurement pulses and receiving the reflections of these pulses on objects in the environment of the vehicle
Implementation Method 4
Based on the time of flight method, the distance to the objects on which the measurement pulses have been reflected can be determined
Data Source
AI summary
A method for optical distance measurements is suggested which comprises carrying out Radar measurements, building a grid map comprising a plurality of elements based on the Radar measurements, extracting information about the dynamic state of each element and assigning the information to the respective element. The method further comprises carrying out Lidar measurements resulting in a Lidar point cloud and associating the Lidar point cloud with the grid map.