Beam Steering Radar Long-Range Mode Adjustment
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Solution Overview
Problem
Current autonomous driving systems face challenges in detecting and classifying objects at long ranges with high accuracy and speed, particularly in varying weather conditions, due to limitations in radar, camera, and lidar sensors, which affect their reliability and efficiency in real-time decision-making.
Innovation Solution
A beam steering radar with an adjustable long-range mode that dynamically controls its electrical or electromagnetic configuration to steer beams and adjust radar scan parameters, combining with other sensors like cameras and lidars, to enhance object detection and identification capabilities over a wide field of view, enabling both high-speed and high-resolution object tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If radar operates in long-range mode with narrow beam, then detection range is improved, but detection speed and coverage area deteriorate
Solution Approach 1:
The radar system dynamically adjusts its operational mode between long-range detection mode and high-speed detection mode based on real-time driving conditions and object priorities. The beam steering antenna electronically switches between narrow long-range beams and wider high-speed scan patterns, allowing the system to adapt its detection characteristics without physical movement or mode switching delays.
2Measurement precision
If radar increases number of chirps for better velocity estimation, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system applies partial action by using a reduced number of chirps (e.g., 5-10 chirps) for initial object detection and velocity estimation, rather than using the full sequence required for maximum precision. This allows the radar to obtain sufficient velocity information for safety-critical decisions without the full processing time required for exhaustive measurement sequences.
3Area of stationary object
If radar scans entire field of view continuously, then coverage area is improved, but detection speed for specific objects deteriorates
Solution Approach 1:
The field of view is segmented into multiple zones with different detection priorities and beam patterns. High-priority zones (such as direct path ahead, blind spots) receive focused narrow beams with more chirps for detailed velocity estimation, while lower-priority zones use wider beams with fewer chirps. This segmentation allows simultaneous coverage of the entire field of view while maintaining high detection speed for critical areas.
4Length of stationary object
If radar uses high gain narrow beam, then long-range detection is improved, but field of view and object classification capability deteriorate
Solution Approach 1:
The radar employs periodic action by performing multiple scan passes with different beam patterns. Initial passes use narrow high-gain beams for long-range detection, followed by subsequent passes using wider beams to gather additional data for object classification. This periodic switching between narrow and wide beam patterns allows the system to first detect distant objects and then classify them with greater field of view coverage.
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 solution enables fast and accurate detection of objects up to 300 meters or more, improving the reliability of autonomous vehicle operations by providing a 360° true 3D vision and human-like interpretation of the environment, while reducing processing time and maintaining antenna gain, thus enhancing the overall performance in diverse driving scenarios.
Implementation Method 1
A beam steering radar with an adjustable long-range mode for use in autonomous vehicles is disclosed.
Implementation Method 2
the radar adjusts its LRR mode to increase the number of chirps in the radar signal and improve the velocity estimation for the detected objects
Data Source
AI summary
Examples disclosed herein relate to a beam steering radar for use in an autonomous vehicle. The beam steering radar has a radar module with at least one beam steering antenna, a transceiver, and a controller that can cause the transceiver to perform, using the at least one beam steering antenna, a first scan of a field-of-view (FoV) with a first number of chirps in a first radio frequency (RF) signal and a second scan of the FoV with a second number of chirps in a second RF signal. The radar module also has a perception module having a machine learning-trained classifier that can detect objects in a path and surrounding environment of the autonomous vehicle based on the first number of chirps in the first RF signal and classify the objects based on the second number of chirps in the second RF signal.