Autonomous Trailer Localization with Sector-Guided LIDAR Pose Estimation
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Solution Overview
Problem
Existing autonomy-related technologies face challenges in accurately localizing an autonomous tractor-trailer, particularly the trailer, due to the computational resource-intensive process of identifying it within LIDAR data, which includes saturated data points and requires extensive processing.
Innovation Solution
The method involves generating trailer pose instances using sector areas predicted to include the trailer based on LIDAR data, utilizing phase coherent and polarized LIDAR sensors to reduce search space and mitigate saturated data points, thereby conserving computational resources and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional LIDAR components and full LIDAR data processing are used to identify the trailer, then comprehensive data coverage is achieved, but computational resources are wasted and processing efficiency decreases
Solution Approach 1:
The patent divides the full LIDAR data instance into multiple sector areas, each corresponding to a specific angular range. Instead of processing all LIDAR data, the system segments the search space to only process sectors where the trailer is predicted to be located, thereby reducing computational load while maintaining identification accuracy.
Solution Approach 2:
The patent applies different processing strategies to different sectors of LIDAR data. Sectors predicted to contain the trailer receive focused processing attention, while other sectors are either processed with reduced complexity or excluded entirely. This local differentiation optimizes resource allocation based on predicted trailer location.
2Productivity
If phase coherent LIDAR component is used with motion-compensated point clouds, then search space is reduced and processing efficiency improves, but system complexity increases
Solution Approach 1:
The patent performs motion compensation on LIDAR point clouds before processing the data to identify the trailer. By pre-compensating for the movement of the autonomous tractor-trailer system, the search space is reduced and trailer identification becomes more efficient. This preliminary action prepares the data in advance to eliminate the need for complex real-time motion correction during trailer detection.
3Measurement precision
If polarization LIDAR component is used to process LIDAR data, then saturated data points are mitigated and trailer pose accuracy improves, but device complexity increases
Solution Approach 1:
The patent utilizes polarization-sensitive LIDAR components that can detect the polarization state of reflected light. By measuring polarization parameters, the system can distinguish between saturated data points (caused by highly reflective surfaces) and valid trailer reflections. This parameter-based differentiation allows the system to filter out saturated points and improve trailer pose estimation accuracy without requiring fundamentally different sensing technology.
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 reduces computational waste by focusing processing on specific LIDAR data subsets, enhancing trailer localization accuracy and reliability, and enabling effective control of the autonomous tractor-trailer.
Implementation Method 1
obtaining a Light Detection and Ranging (LIDAR) data instance of LIDAR data, the LIDAR data being generated by one or more LIDAR sensors
Implementation Method 2
utilizing phase coherent and polarized LIDAR sensors to reduce search space and mitigate saturated data points
Implementation Method 3
utilizing phase coherent and polarized LIDAR sensors to reduce search space and mitigate saturated data points
Data Source
AI summary
Systems and methods for localization of a trailer of an autonomous tractor-trailer are described herein. Some implementations can determine a sector area in an environment of the autonomous tractor-trailer that is predicted to include the trailer, determine a subset of an LIDAR data that is generated by LIDAR sensor(s) of an autonomous tractor of the autonomous tractor-trailer and that is predicted to include the trailer based on the sector area, generate a trailer pose instance of a trailer pose of the trailer based on the subset of the LIDAR data, and cause the trailer pose instance to be utilized in controlling the autonomous tractor-trailer. Additional or alternative implementations can utilize particular LIDAR sensor(s) in generating the trailer pose instance, such as phase coherent LIDAR sensor(s) or polarized LIDAR sensor(s).