Autonomous Vehicle LADAR Navigation with Adaptive Buffer and Fusion
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Solution Overview
Problem
Autonomous land vehicle navigation systems face challenges in accurately classifying road elements and obstacles due to the diversity of vehicle states and road configurations, particularly when processing LADAR data and combining GPS/INS data, leading to varying degrees of inaccuracy, especially at different speeds and in complex terrain.
Innovation Solution
A system and method utilizing a laser scanner and geo-location unit with a processor that performs range and intensity scans, applying ground plane-based, range-based, and intensity-based processing modules to classify road and obstacle data, and a classification fusion module to provide robust navigation information, including lane markings, enabling safe route planning and maneuvering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If a fixed-size history buffer is employed for processing LADAR scans, then the system can maintain a consistent data structure, but the classification accuracy varies with vehicle speed causing inaccuracy in ground plane estimation
Solution Approach 1:
The patent applies dynamics by making the history buffer size adaptive rather than fixed. The buffer dynamically adjusts its size based on vehicle speed and terrain complexity, allowing the system to maintain data structure consistency while adapting to varying operational conditions. This resolves the contradiction by enabling the buffer to expand during high-speed operation to capture sufficient ground samples, and contract during low-speed operation to maintain classification accuracy.
2Measurement precision
If multiple processing modules are used to classify road elements, then the classification accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the classification task into multiple specialized processing modules, each handling specific aspects of road element classification. The system segments classification into ground plane estimation, obstacle detection, and road marking identification modules. This segmentation improves overall classification accuracy by allowing each module to specialize in specific features while reducing the complexity burden on any single module through functional decomposition.
Solution Approach 2:
The patent applies merging by integrating the outputs of multiple processing modules through a classification fusion module. This fusion module combines results from ground plane estimation, obstacle detection, and road marking identification to produce a unified classification. The merging approach maintains high classification accuracy by leveraging complementary information from multiple modules while managing system complexity through a centralized integration point.
3Loss of information
If LADAR reflectance measurements are used to derive road markings, then the system can provide additional navigation information, but the processing complexity increases
Solution Approach 1:
The patent applies preliminary action by performing reflectance-based road marking detection as a separate preliminary processing step before final classification. The system first processes LADAR reflectance measurements to identify potential road markings, then integrates these results into the overall classification framework. This preliminary processing approach ensures complete navigation information is captured while managing complexity by handling reflectance analysis independently before merging with other classification results.
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 system provides accurate situation awareness for autonomous vehicle navigation, allowing for safe traversal of varied terrain by effectively classifying traversable areas, obstacles, and road markings, even at high speeds and during complex maneuvers, enhancing the vehicle's ability to plan and execute safe routes.
Implementation Method 1
laser detection and ranging (LADAR) sensors. LADAR is also referred to as LIDAR (Light Detection and Ranging)
Data Source
AI summary
A system and method for providing information for autonomous vehicle navigation are disclosed. The system comprises at least one laser scanner configured to perform one or more range and intensity scans of an area around the autonomous vehicle, and a geo-location unit comprising one or more global positioning system sensors and inertial navigation system sensors. The system also includes at least one processor in operative communication with the laser scanner and the geo-location unit. The processor is configured to execute one or more program modules comprising a ground plane-based processing module configured to receive range scan data transformed into world coordinates, and output ground plane-based classification data; a range-based processing module configured to receive data from a single range scan, and output range-based classification data; an intensity-based processing module configured to receive intensity scan data, and output intensity-based classification data; and a classification fusion module configured to receive the classification data from each of the processing modules, and output range bin classification data.