Survey on LiDAR Perception in Adverse Weather Conditions

Dreissig, Mariella; Scheuble, Dominik; Piewak, Florian; Boedecker, Joschka

doi:10.1109/iv55152.2023.10186539

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Other2

Book1

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 19 publications

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Text2LiDAR: Text-Guided LiDAR Point Cloud Generation via Equirectangular Transformer

Wu,

Zhang,

Qian

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Text2LiDAR: Text-Guided LiDAR Point Cloud Generation via Equirectangular Transformer

Wu,

Zhang,

Qian

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Safety-First Autonomous Vehicle Technology: Empirical Assessment of Sensor Performance in Diverse Environmental Conditions

Kim,

Moon,

Kim

et al. 2024

KSCE J Civ Eng

View full text Add to dashboard Cite

Machine vision-based autonomous road hazard avoidance system for self-driving vehicles

Qiu,

Tang,

Yang

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The resolution of traffic congestion and personal safety issues holds paramount importance for human’s life. The ability of an autonomous driving system to navigate complex road conditions is crucial. Deep learning has greatly facilitated machine vision perception in autonomous driving. Aiming at the problem of small target detection in traditional YOLOv5s, this paper proposes an optimized target detection algorithm. The C3 module on the algorithm’s backbone is upgraded to the CBAMC3 module, introducing a novel GELU activation function and EfficiCIoU loss function, which accelerate convergence on position loss lbox, confidence loss lobj, and classification loss lcls, enhance image learning capabilities and address the issue of inaccurate detection of small targets by improving the algorithm. Testing with a vehicle-mounted camera on a predefined route effectively identifies road vehicles and analyzes depth position information. The avoidance model, combined with Pure Pursuit and MPC control algorithms, exhibits more stable variations in vehicle speed, front-wheel steering angle, lateral acceleration, etc., compared to the non-optimized version. The robustness of the driving system's visual avoidance functionality is enhanced, further ameliorating congestion issues and ensuring personal safety.

show abstract

Survey on LiDAR Perception in Adverse Weather Conditions

Cited by 19 publications

References 63 publications

Text2LiDAR: Text-Guided LiDAR Point Cloud Generation via Equirectangular Transformer

Text2LiDAR: Text-Guided LiDAR Point Cloud Generation via Equirectangular Transformer

Safety-First Autonomous Vehicle Technology: Empirical Assessment of Sensor Performance in Diverse Environmental Conditions

Machine vision-based autonomous road hazard avoidance system for self-driving vehicles

Contact Info

Product

Resources

About