LiDAR TDMA Scanning for Crosstalk-Resistant Point Clouds
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Solution Overview
Problem
Lidar devices experience crosstalk noise due to high-intensity return signals from reflective objects, leading to false positive detections and missed detections, which can impair the accuracy of object detection and navigation.
Innovation Solution
Implement a two-cycle emission and detection strategy in lidar devices, where all channels emit and detect signals in a first cycle for long-range detection, and subsets of channels emit and detect signals in a second cycle for short-range detection, allowing for crosstalk identification and mitigation by comparing results from both cycles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If all channels emit and detect signals simultaneously in a single cycle, then the detection speed is high, but crosstalk noise occurs due to high-intensity return signals from reflective objects
Solution Approach 1:
The patent segments the detection process into multiple cycles with different listening window durations. The first cycle uses a long listening window to detect all channels simultaneously for high-speed detection, while the second cycle uses a short listening window with selective channel activation to identify and mitigate crosstalk sources, thereby resolving the contradiction between detection speed and crosstalk noise
Solution Approach 2:
The patent implements periodic action by alternating between two types of detection cycles: a first cycle with all channels active and a long listening window, and a second cycle with selective channel activation and a short listening window. This periodic switching enables the system to maintain high detection speed while periodically identifying and mitigating crosstalk noise
2Length of stationary object
If the listening window duration is increased to detect long-range objects, then the detection range is extended, but the likelihood of detecting crosstalk signals increases
Solution Approach 1:
The patent segments the listening window into two distinct durations: a first (longer) listening window for detecting long-range objects and a second (shorter) listening window for identifying crosstalk signals. This segmentation allows the system to extend detection range while separately managing crosstalk detection probability
Solution Approach 2:
The patent uses the second cycle with selective channel activation as an intermediary mechanism to identify crosstalk sources. By comparing detections from the first cycle (all channels) with the second cycle (selective channels), the system can distinguish valid long-range detections from crosstalk artifacts
3Object-affected harmful factors
If subsets of channels are activated sequentially for short-range detection, then crosstalk is reduced, but the detection complexity increases
Solution Approach 1:
The patent applies periodic action by implementing a repeating two-cycle pattern: the first cycle activates all channels for comprehensive detection, and the second cycle activates subsets of channels selectively for crosstalk mitigation. This periodic structure manages detection complexity through predictability and systematic channel activation patterns
4Measurement precision
If two-cycle emission and detection strategy is implemented, then crosstalk identification is improved, but the detection time increases
Solution Approach 1:
The patent applies partial action by activating only necessary subsets of channels during the second cycle rather than all channels. This selective activation maintains crosstalk identification accuracy while reducing the time overhead compared to a full two-cycle approach with all channels active in both cycles
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 crosstalk noise, enhances detection accuracy by distinguishing valid signals from crosstalk, and improves the reliability of point cloud generation for autonomous vehicles.
Implementation Method 1
a first group of reflected light signals corresponds to reflections of the first group of light signals from objects in the surrounding environment
Data Source
AI summary
Example embodiments relate to time-division multiple access scanning for crosstalk mitigation in light detection and ranging (lidar) devices. An example embodiment includes a method. The method includes emitting a first group of light signals into a surrounding environment. The first group of light signals corresponds to a first angular resolution. The method also includes detecting, during a first listening window, a first group of reflected light signals. Additionally, the method includes emitting a second group of light signals into the surrounding environment. The second group of light signals corresponds to a second angular resolution with respect to the surrounding environment. The second angular resolution is lower than the first angular resolution. Further, the method includes detecting a second group of reflected light signals from the surrounding environment. In addition, the method includes synthesizing, by a controller of the lidar device, a dataset usable to generate one or more point clouds.