Autonomous Vehicle LIDAR Waveform Analysis for Spurious Objects
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Solution Overview
Problem
Autonomous vehicles face challenges in distinguishing between actual objects and spurious objects, such as vehicle exhaust, dust, rain, snow, or fog, which can be misinterpreted by LIDAR sensors due to similar waveform data, leading to potential misinterpretation and inappropriate driving decisions.
Innovation Solution
A method involving the use of training data and heuristics to identify spurious objects based on waveform characteristics, such as peak elongation and number of peaks, to filter out irrelevant data points, allowing the vehicle's computing systems to make more accurate driving decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LIDAR sensors are used to detect objects in autonomous vehicles, then the vehicle can perceive its surroundings, but spurious objects such as exhaust, dust, rain, snow, or fog cannot be distinguished from solid objects
Solution Approach 1:
The patent transitions from analyzing only intensity information (1D) to utilizing waveform data (time-domain signal progression) to distinguish spurious objects. By examining the temporal evolution of LIDAR return signals, the system can differentiate between solid objects and spurious objects like rain or exhaust that exhibit characteristic waveform patterns.
Solution Approach 2:
The patent changes the analytical parameters from simple intensity values to waveform characteristics including peak elongation, number of peaks, and peak width. These parameter transformations enable the detection system to identify spurious objects based on their distinctive temporal signal patterns rather than just their reflectivity.
2Reliability
If the vehicle responds to all detected LIDAR objects, then safety is prioritized, but false responses to spurious objects reduce driving efficiency
Solution Approach 1:
The patent extracts and filters out spurious objects from the detected LIDAR data by identifying their characteristic waveform patterns. This selective removal of false objects allows the vehicle to respond only to genuine obstacles, maintaining safety while eliminating unnecessary braking or steering actions that would reduce driving efficiency.
3Measurement precision
If waveform data is used to identify spurious objects, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the waveform analysis into distinct measurable parameters (peak elongation, number of peaks, peak width) that can be independently calculated. This segmentation of the computational task into discrete, standardized measurements reduces overall complexity while maintaining the ability to accurately identify spurious objects through multiple characteristic features.
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 accuracy of object detection by effectively differentiating between solid and spurious objects, improving the vehicle's ability to navigate through environments with challenging weather conditions or debris, thereby ensuring safer and more efficient autonomous driving.
Implementation Method 1
LIDAR sensor of a vehicle... each given LIDAR data point including location information and intensity information and being associated with waveform data
Data Source
AI summary
Aspects of the disclosure relate to detecting spurious objects. For instance, a model may be trained using raining data including a plurality of LIDAR data points generated by a LIDAR sensor of a vehicle. Each given LIDAR data point includes location information and intensity information, and is associated with waveform data for that given LIDAR data point. At least one of the plurality of LIDAR data points is further associated with a label identifying spurious objects through which the vehicle is able to drive. The model and/or a plurality of heuristics may then be provided to a vehicle in order to allow the vehicle to determine LIDAR data points that correspond to spurious objects. These LIDAR data points may then be filtered from sensor data, and the filtered sensor data may be used to control the vehicle in an autonomous driving mode.