Variable Density Lidar Point Cloud Generation
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Solution Overview
Problem
Current LIDAR systems for autonomous vehicles face limitations in generating reliable point cloud data due to restricted illumination power to ensure eye safety, which affects their ability to detect far-away objects under varying environmental conditions such as rain, fog, and darkness.
Innovation Solution
A LIDAR system that includes a processor controlling a light source to emit multiple light bursts, directing them through a light deflector to scan a field of view, and receiving reflection signals to determine and generate point cloud data points, allowing for enhanced detection capabilities while maintaining eye safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the illumination power of LIDAR systems is increased to improve detection of far-away objects, then the detection capability is improved, but the eye safety is compromised
Solution Approach 1:
The LIDAR system emits light in periodic bursts rather than continuous waves. Each burst consists of multiple pulses that are modulated in intensity. This periodic emission allows the system to achieve high peak powers for improved detection while maintaining low average power to ensure eye safety. The processor controls the timing and intensity of each pulse within the burst to optimize both detection capability and safety.
Solution Approach 2:
The system dynamically changes multiple parameters including pulse duration, pulse repetition frequency, and intensity modulation depth. By adjusting these parameters, the system can adapt to different detection requirements while maintaining eye safety. The processor selectively determines the number of point cloud data points to generate based on received reflection signals, optimizing the balance between detection performance and safety for each light burst.
2Object-affected harmful factors
If the illumination power is limited to ensure eye safety, then the eye safety is maintained, but the detection of far-away objects is affected
Solution Approach 1:
The system uses periodic light bursts with multiple modulated pulses to achieve high peak powers during brief intervals, improving far-away object detection. Between bursts, the system returns to lower power states, maintaining eye safety. This periodic structure allows the system to overcome the power limitation while preserving safety constraints.
Solution Approach 2:
The processor pre-determines the optimal number of light bursts and pulses to emit based on expected detection requirements before actual emission. This preliminary planning allows the system to prepare appropriate intensity and timing parameters that maximize detection capability within safety limits, rather than reacting after power limitations are encountered.
3Reliability
If multiple light bursts with multiple pulses are emitted to improve point cloud data generation, then the detection reliability is improved, but the system complexity increases
Solution Approach 1:
The light emission is segmented into multiple bursts, each containing multiple pulses. This segmentation allows the system to process and analyze reflection signals from different temporal and spatial segments, improving point cloud data quality. The processor selectively determines data point generation for at least one light burst based on received signals, managing complexity by processing segments independently rather than as a monolithic system.
Solution Approach 2:
The system dynamically adjusts the number of light bursts, pulses per burst, and intensity parameters based on real-time feedback from reflection signals. This dynamic adaptation allows the system to optimize detection performance without requiring fixed complex hardware configurations. The processor's selective determination of data points based on received signals provides flexibility that reduces overall system complexity.
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 improves the generation of point cloud data points, enabling more reliable detection of objects in diverse environmental conditions without compromising eye safety, thereby enhancing the performance of LIDAR systems in autonomous vehicles.
Implementation Method 1
A light detection and ranging system, (LIDAR a/k/a LADAR) is an example of technology that can work well in differing conditions, by measuring distances to objects by illuminating objects with light and measuring the reflected pulses with a sensor.
Data Source
AI summary
LIDAR systems and methods for generating point cloud data points using LIDAR systems are provided. In one implementation, a LIDAR system may include a processor programmed to control at least one light source configured to emit a plurality of light bursts for scanning a field of view, wherein each of the plurality of light bursts includes a plurality of light pulses. The processor is further configured to receive, from at least one sensor, reflection signals associated with the plurality of light pulses included in the plurality of light bursts. The processor is further programmed to selectively determine a number of point cloud data points to generate based on the received reflection signals associated with the plurality of light pulses included in at least one light burst. Then, the processor is programmed to output the determined number of point cloud data points generated for the at least one light burst.