A Spatiotemporal Deep Learning Architecture for Road Surface Classification Using LiDAR in Autonomous Emergency Braking Systems
This paper proposes a spatiotemporal architecture with a deep neural network (DNN) for road surface conditions and types classification using LiDAR for autonomous emergency braking (AEB) systems. It is known that LiDAR provides information on the reflectivity and number of point clouds depending on...
Main Authors: | Ju Won Seo, Jin Sung Kim, Jin Ho Yang, Chung Choo Chung |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10286502/ |
Similar Items
-
ZUST Campus: A Lightweight and Practical LiDAR SLAM Dataset for Autonomous Driving Scenarios
by: Yuhang He, et al.
Published: (2024-04-01) -
Robust LiDAR-Based Vehicle Detection for On-Road Autonomous Driving
by: Xianjian Jin, et al.
Published: (2023-06-01) -
Adversarial robustness analysis of LiDAR-included models in autonomous driving
by: Bo Yang, et al.
Published: (2024-03-01) -
Forest Roads Mapped Using LiDAR in Steep Forested Terrain
by: Russell A. White, et al.
Published: (2010-04-01) -
Synthesis of LiDAR-Detectable True Black Core/Shell Nanomaterial and Its Practical Use in LiDAR Applications
by: Suk Jekal, et al.
Published: (2022-10-01)