Visualization of 3D Point Clouds for Vehicle Detection Based on LiDAR and Camera Fusion

Madake, Jyoti; Rane, Rushikesh; Rathod, Rohan; Sayyed, Alfisher; Bhatlawande, Shripad; Shilaskar, Swati

doi:10.1109/ocit56763.2022.00115

Cited by 1 publication

(1 citation statement)

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“…Moreover, the available models predominantly focus on camera and video data or hypothetical scenarios, overlooking the potential of LiDAR point clouds. These models also fail to harness the fusion of multiple sensory inputs, such as LiDARs and cameras, which can be used to advance object detection for AVs [ 47 ]. Additionally, existing models calculate the complexity in arbitrary regions of interest around the AV without accounting for the specific sensor specifications.…”

Section: Literature Reviewmentioning

confidence: 99%

Leveraging LiDAR-Based Simulations to Quantify the Complexity of the Static Environment for Autonomous Vehicles in Rural Settings

Abohassan,

El-Basyouny

2024

Sensors

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This paper uses virtual simulations to examine the interaction between autonomous vehicles (AVs) and their surrounding environment. A framework was developed to estimate the environment’s complexity by calculating the real-time data processing requirements for AVs to navigate effectively. The VISTA simulator was used to synthesize viewpoints to replicate the captured environment accurately. With an emphasis on static physical features, roadways were dissected into relevant road features (RRFs) and full environment (FE) to study the impact of roadside features on the scene complexity and demonstrate the gravity of wildlife–vehicle collisions (WVCs) on AVs. The results indicate that roadside features substantially increase environmental complexity by up to 400%. Increasing a single lane to the road was observed to increase the processing requirements by 12.3–16.5%. Crest vertical curves decrease data rates due to occlusion challenges, with a reported average of 4.2% data loss, while sag curves can increase the complexity by 7%. In horizontal curves, roadside occlusion contributed to severe loss in road information, leading to a decrease in data rate requirements by as much as 19%. As for weather conditions, heavy rain increased the AV’s processing demands by a staggering 240% when compared to normal weather conditions. AV developers and government agencies can exploit the findings of this study to better tailor AV designs and meet the necessary infrastructure requirements.

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Section: Literature Reviewmentioning

confidence: 99%