Abstract
Mobile pulse light detection and ranging (LIDAR) is an essential component of autonomous vehicles. The obstacle detection function of autonomous vehicles requires very low failure rates. With an increasing number of autonomous vehicles equipped with LIDAR sensors for use in the detection and avoidance of obstacles and for safe navigation through the environment, the probability of mutual interference becomes an important issue. The reception of foreign laser pulses can lead to problems such as ghost targets or a reduced signal-to-noise ratio (SNR). In this paper, we presented the probability that any LIDAR sensor would interfere mutually by considering spatial and temporal overlaps. We presented some typical mutual interference scenarios in real-world vehicle applications, as well as an analysis of the interference mechanism. We proposed a new multi-plane LIDAR sensor which used coded pulse streams encoded by carrier-hopping prime code (CHPC) technology to measure surrounding perimeters without mutual interference. These encoded pulses utilized a random azimuth identification and checksum with random spreading code. We modeled the entirety of the LIDAR sensor operation in Synopsys OptSim and represented the alien pulse elimination functionality obtained via modeling and simulation.
Highlights
Academic Editor: Andrzej StatecznyRange sensors are devices that capture the three-dimensional (3-D) structure of the world from the viewpoint of the sensor, usually measuring distances to closest targets [1,2,3,4,5].These measurements could be across a scanning plane or a 3-D image with distance measurements at every point.These range sensors have been used for many years in the fields of localization, obstacle detection and tracking for autonomous vehicles [6,7]
We modeled the entirety of the light detection and ranging (LIDAR) sensor operation in Synopsys OptSim with Mathworks MATLAB
In the EU, through the MOSAIM project, a study was performed on how mutual interference occurs in vehicle radar sensors
Summary
Range sensors are devices that capture the three-dimensional (3-D) structure of the world from the viewpoint of the sensor, usually measuring distances to closest targets [1,2,3,4,5]. These measurements could be across a scanning plane or a 3-D image with distance measurements at every point These range sensors have been used for many years in the fields of localization, obstacle detection and tracking for autonomous vehicles [6,7]. The multiple return separation algorithm requires a consistent and known relationship between the cameras’ amplitudes and phase responses at both modulation frequencies This type of calibration was not available for these cameras, so it was carried out by comparing manually selected regions of the image judged to be minimally affected by multipath interference. With the increase in the number of autonomous vehicles equipped with LIDAR sensors for use in obstacle detection and avoidance for safe navigation through environments, the possibility of mutual interference becomes an issue.
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