Autonomous driving based on accurate localization using multilayer LiDAR and dead reckoning

By Naoki Akai, Luis Yoichi Morales Saiki, Takuma Yamaguchi, Eijiro Takeuchi, Yuki Yoshihara, Hiroyuki Okuda, Tatsuya Suzuki, Yoshiki Ninomiya. 2017

69 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Automotive Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Pravendra Singh
2. Baofu Wu
3. Sidi Lu
4. Ren Zhong
5. Qingyan Zhang
6. Yongtao Yao
7. Pranav Singh Chib
8. Weisong Shi

In Automotive Engineering

1. Pravendra Singh
2. Baofu Wu
3. Sidi Lu
4. Ren Zhong
5. Qingyan Zhang
6. Yongtao Yao
7. Pranav Singh Chib
8. Weisong Shi

In Computer Vision and Pattern Recognition

1. Pravendra Singh
2. Baofu Wu
3. Sidi Lu
4. Ren Zhong
5. Qingyan Zhang
6. Yongtao Yao
7. Pranav Singh Chib
8. Weisong Shi

Citations in the corpus

• Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, background): “ased and end-to-end based. Modular design decouples the localization, perception, control, etc. as separate modules and make it possible for people with different background work together [14], [15], [16], [17], [18]. End-toend based design is largely motivated by the development of artificial intell”
• Recent Advancements in End-to-End Autonomous Driving Using Deep Learning: A Survey (2023, methodology): “The modular pipeline starts by inputting raw sensory data to the perception module for obstacle detection [30] and localization via the localization module [3].”