# Composition and Application of Current Advanced Driving Assistance System: A Review

arXiv: https://arxiv.org/abs/2105.12348 · HTML: https://mlsanity.com/paper/2105.12348/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 83 references that are in the map.
- First posted on arXiv 26 May 2021.

## Builds on (most cited first, 16 of 16)

- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1812.07179/) (2018)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https://mlsanity.com/paper/1902.07830/) (2019)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [Learning Lightweight Lane Detection CNNs by Self Attention Distillation](https://mlsanity.com/paper/1908.00821/) (2019)
- [Self-Driving Cars: A Survey](https://mlsanity.com/paper/1901.04407/) (2019)
- [A Survey of Deep Learning Applications to Autonomous Vehicle Control](https://mlsanity.com/paper/1912.10773/) (2019)
- [3D Object Proposals using Stereo Imagery for Accurate Object Class Detection](https://mlsanity.com/paper/1608.07711/) (2016)
- [Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection](https://mlsanity.com/paper/1802.09972/) (2018)
- [LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks](https://mlsanity.com/paper/1809.07941/) (2018)
- [Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks](https://mlsanity.com/paper/1903.02193/) (2019)
- [Progressive LiDAR Adaptation for Road Detection](https://mlsanity.com/paper/1904.01206/) (2019)
- [YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud](https://mlsanity.com/paper/1808.02350/) (2018)
- [Dynamic SLAM: The Need For Speed](https://mlsanity.com/paper/2002.08584/) (2020)
- [Computer Stereo Vision for Autonomous Driving](https://mlsanity.com/paper/2012.03194/) (2020)

## Built on by (most cited first, 1 of 1)

- [3D Human Pose and Shape Estimation from LiDAR Point Clouds: A Review](https://mlsanity.com/paper/2509.12197/) (2025)

## Abstract (arXiv)

Due to the growing awareness of driving safety and the development of sophisticated technologies, advanced driving assistance system (ADAS) has been equipped in more and more vehicles with higher accuracy and lower price. The latest progress in this field has called for a review to sum up the conventional knowledge of ADAS, the state-of-the-art researches, and novel applications in real-world. With the help of this kind of review, newcomers in this field can get basic knowledge easier and other researchers may be inspired with potential future development possibility. This paper makes a general introduction about ADAS by analyzing its hardware support and computation algorithms. Different types of perception sensors are introduced from their interior feature classifications, installation positions, supporting ADAS functions, and pros and cons. The comparisons between different sensors are concluded and illustrated from their inherent characters and specific usages serving for each ADAS function. The current algorithms for ADAS functions are also collected and briefly presented in this paper from both traditional methods and novel ideas. Additionally, discussions about the definition of ADAS from different institutes are reviewed in this paper, and future approaches about ADAS in China are introduced in particular.