Phase-Coherent LiDAR Object Classification for Autonomous Vehicle Control
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately interpreting environmental data and adapting control strategies to navigate complex environments safely, particularly in distinguishing and responding to various objects with different classifications and velocities.
Innovation Solution
A method utilizing phase coherent LIDAR data to determine the classification and velocity of objects in a vehicle's environment, incorporating machine learning models and adaptive control techniques to adjust vehicle motion accordingly, allowing for precise autonomous control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If phase coherent LIDAR data is used to determine object classification and velocity, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces traditional mechanical classification systems with phase coherent LIDAR technology that uses optical phase measurement to determine both range and velocity simultaneously. This substitution enables more precise measurement without requiring complex mechanical scanning or multiple separate sensors, as the phase information inherently encodes velocity data through the Doppler effect.
Solution Approach 2:
The system changes the measurement parameter from simple time-of-flight (range only) to phase coherence measurement, which provides both range and velocity information from the same LIDAR signal. By analyzing the phase shift of the returned light signal, the system extracts velocity data without requiring additional hardware, thus improving measurement precision while managing device complexity.
2Reliability
If autonomous control adapts based on object classification and velocity, then reliability is improved, but device complexity increases
Solution Approach 1:
The autonomous control system implements continuous feedback loops where LIDAR measurements of object classification and velocity are constantly fed back to adjust vehicle control parameters. This feedback mechanism enables the system to adapt to changing environmental conditions in real-time, improving reliability by responding to actual sensor data rather than relying on pre-programmed responses.
Solution Approach 2:
The control system transitions from static, pre-programmed control strategies to dynamic adaptation based on real-time object classification and velocity data. The system continuously adjusts control parameters such as acceleration, steering, and braking based on the classified objects and their measured velocities, enabling flexible and reliable autonomous navigation in diverse environments.
3Measurement precision
If velocity determination is incorporated into LIDAR data processing, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary processing of the LIDAR signal to extract phase information before full data analysis is required. By pre-processing the raw LIDAR returns to identify phase shifts and estimate velocities early in the processing pipeline, the system reduces the computational burden on subsequent processing stages, thereby minimizing additional processing time 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
Enhances the ability of autonomous vehicles to safely navigate by accurately identifying and responding to objects, improving safety and efficiency in diverse environmental conditions.
Implementation Method 1
receiving, from a phase coherent Light Detection and Ranging (LIDAR) component of a vehicle, a group of phase coherent LIDAR data points
Implementation Method 2
Light Detection and Ranging (LIDAR) component
Implementation Method 3
Each of the phase coherent LIDAR data points of the group indicate a corresponding range and a corresponding velocity
Data Source
AI summary
Determining classification(s) for object(s) in an environment of autonomous vehicle, and controlling the vehicle based on the determined classification(s). For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be controlled based on determined pose(s) and/or classification(s) for objects in the environment. The control can be based on the pose(s) and/or classification(s) directly, and/or based on movement parameter(s), for the object(s), determined based on the pose(s) and/or classification(s). In many implementations, pose(s) and/or classification(s) of environmental object(s) are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.