Research on Comparison of LiDAR and Camera in Autonomous Driving

Wang, Pei-De

doi:10.1088/1742-6596/2093/1/012032

Cited by 33 publications

(14 citation statements)

References 4 publications

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“…The point feature techniques extract valuable data to detect an object using data points. 12 Finally, the 3D point clouds project on the ground and connected-component labeling approach to group the things.…”

Section: Lidar and Camera Fusionmentioning

confidence: 99%

Real-time pedestrian detection using radar-camera fusion and clustering

Arudpiragasam,

Kannuri,

Schwarz

et al. 2023

Multimodal Image Exploitation and Learning 2023

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The design and development of autonomous vehicles ensure to move safely on roads while focusing on pedestrian detection systems has brought convention so that pedestrians can be detected quickly and precisely. Moreover, the researchers have mentioned that pedestrian skin detection has proven to be a tough challenge since skin color can vary in appearance due to various factors such as weather conditions, sun-lighting, occlusion, race, etc. Thus, the detection of pedestrian skin tone has not been thoroughly explored in some circumstances to detect pedestrians; they used the different characteristics of all pedestrians as one sample. The evidence is that pedestrian skin tones fit the Fitzpatrick scale of 4 to 6, which is hard to detect with an existing state-of-art algorithm. Our proposed methodology, the radar-camera fusion technique, is used to predict the obstacle in any scenario. A convolution neural network extracts pedestrian features from RGB images and radar data. Also, we have introduced data preparation and feature extraction. We feature mapping to get more detection accuracy and clustering to find the similarities between features that will attain darker skin pedestrian details.

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Section: Lidar and Camera Fusionmentioning

confidence: 99%

Real-time pedestrian detection using radar-camera fusion and clustering

Arudpiragasam,

Kannuri,

Schwarz

et al. 2023

Multimodal Image Exploitation and Learning 2023

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“…Several main types of sensors are used as a source of information for object detection in an AV: LiDARs, mono and stereo cameras. Each type has advantages and disadvantages that are described in detail in [1]. LiDARs provide precise threedimensional environment representation, but they are still quite expensive and have a limited lifespan.…”

Section: Introductionmentioning

confidence: 99%

L-shape fitting algorithm for 3D object detection in bird’s-eye-view in an autonomous driving system

Chekanov,

Shipitko

2024

Sixteenth International Conference on Machine Vision (ICMV 2023)

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The ability of autonomous vehicles (AVs) to detect three-dimensional objects is crucial for motion planning, object tracking and safe driving. This task is especially challenging for systems using only monocular cameras, for which depth estimation presents special difficulties. In this paper, we discuss the subsystem of 3D object detection in bird's-eye-view (BEV) for a single camera in an AV system. The subsystem consists of two parts. First, it estimates the contour of the object's projection polygon in BEV based on 2D detection and drivable area segmentation (a planar ground model is used). Second, it simplifies the object's projection by fitting the obtained polygon to a rotated bounding box. For this part we propose a new L-shape model-based fitting algorithm. It assumes that the vertices of the input polygon belong to two adjacent sides of the fitted bounding box. We compared this algorithm with a naive approach which minimizes the bounding box's area and with adaptations of algorithms from a paper solving a similar problem with LiDAR point clouds. The L-shape algorithm outperformed the alternatives.

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“…The utilization of these three main technologies presents a diverse set of advantages and drawbacks. For instance, cameras perform very well in object recognition and can provide the best visual information of the surroundings [7]. However, their performance can be reduced in low-light environments and adverse weather conditions, such as fog, rain, and snow.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds

Roriz,

Silva,

Dias

et al. 2024

Sensors

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In the evolving landscape of autonomous driving technology, Light Detection and Ranging (LiDAR) sensors have emerged as a pivotal instrument for enhancing environmental perception. They can offer precise, high-resolution, real-time 3D representations around a vehicle, and the ability for long-range measurements under low-light conditions. However, these advantages come at the cost of the large volume of data generated by the sensor, leading to several challenges in transmission, processing, and storage operations, which can be currently mitigated by employing data compression techniques to the point cloud. This article presents a survey of existing methods used to compress point cloud data for automotive LiDAR sensors. It presents a comprehensive taxonomy that categorizes these approaches into four main groups, comparing and discussing them across several important metrics.

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Research on Comparison of LiDAR and Camera in Autonomous Driving

Cited by 33 publications

References 4 publications

Real-time pedestrian detection using radar-camera fusion and clustering

Real-time pedestrian detection using radar-camera fusion and clustering

L-shape fitting algorithm for 3D object detection in bird’s-eye-view in an autonomous driving system

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds

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