Coherent Lidar Velocity Estimation for Rigid-Body Motion Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in accurately estimating velocity and tracking objects in dynamic environments due to limitations in existing lidar technologies, which struggle to determine velocities based on a single sensing frame and fail to differentiate between translational and rotational motions effectively.
Innovation Solution
The implementation of Doppler-assisted velocity sensing using coherent lidars, which detect changes in frequency and phase of reflected waves to determine radial velocity, enabling the estimation and tracking of both translational and rotational motions of objects by fitting coordinates and radial velocity data to a rigid body equation, even from a single frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional lidars are used for velocity estimation, then the system can detect object positions, but it cannot accurately determine velocities from a single sensing frame
Solution Approach 1:
The patent uses Doppler frequency shift as an intermediary physical phenomenon to extract velocity information from reflected laser signals. The coherent lidar system measures the frequency shift of reflected light caused by object motion, converting this Doppler effect into velocity data that can be obtained from a single sensing frame without requiring temporal sequences of position measurements.
Solution Approach 2:
The patent changes the measurement parameter from position-only detection to frequency-shift detection. By using coherent detection to measure the Doppler frequency shift of reflected laser light, the system directly obtains velocity information as a measurable parameter, transforming the inability to measure velocity into a capability to measure frequency shifts that correspond to velocity.
2Measurement precision
If velocity sensing is implemented using coherent lidars, then radial velocity can be detected, but the system cannot differentiate between translational and rotational motions
Solution Approach 1:
The patent segments the velocity measurement into multiple independent radial velocity components by using multiple lidars positioned at different locations. Each lidar measures the radial velocity along its own line of sight, and the system processes these segmented measurements separately before combining them to reconstruct the full motion state, including both translational and rotational components.
Solution Approach 2:
The patent adds spatial dimensionality to the velocity measurement system by deploying multiple lidars at different positions and orientations. This multi-dimensional arrangement allows the system to capture velocity information from multiple directions simultaneously, enabling the differentiation between translational motion (affecting all sensors similarly) and rotational motion (affecting sensors differently based on their positions).
3Measurement precision
If multiple lidars are used to capture full motion information, then both translational and rotational velocities can be determined, but the device complexity increases
Solution Approach 1:
The patent makes each individual lidar unit multi-functional by designing it to perform both position detection and velocity measurement through coherent detection. Each lidar simultaneously provides range information and radial velocity information, eliminating the need for separate sensors for different measurement types and reducing overall system complexity despite using multiple units.
Solution Approach 2:
The patent merges the functions of multiple lidars into a unified processing framework that jointly processes position and velocity data from all sensors. By combining the measurements and using a unified mathematical model to estimate both translational and rotational motion, the system achieves full motion characterization while managing complexity through integrated processing rather than separate independent systems.
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 allows for precise velocity estimation and tracking of objects, enhancing the accuracy and safety of autonomous driving by determining the full motion of rigid bodies, including both translational and rotational velocities, even when objects exhibit complex motions like turning or spinning.
Implementation Method 1
Doppler-assisted velocity sensing using coherent lidars, which detect changes in frequency and phase of reflected waves to determine radial velocity
Data Source
AI summary
Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling velocity estimation for efficient object identification and tracking in autonomous vehicle (AV) applications, including: obtaining, by a sensing system of the AV, a plurality of return points, each return point having a velocity value and coordinates of a reflecting region that reflects a signal emitted by the sensing system, identifying an association of the velocity values and the coordinates of return points with a motion of a physical object, the motion being a combination of a translational motion and a rotational motion of a rigid body, and causing a driving path of the AV to be determined in view of the motion of the physical object.