# Key Ingredients of Self-Driving Cars

arXiv: https://arxiv.org/abs/1906.02939 · HTML: https://mlsanity.com/paper/1906.02939/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [A Survey on Simulators for Testing Self-Driving Cars](https://mlsanity.com/paper/2101.05337/) (2021)
- [Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review](https://mlsanity.com/paper/2308.06419/) (2023)
- [Analyzing General-Purpose Deep-Learning Detection and Segmentation Models with Images from a Lidar as a Camera Sensor](https://mlsanity.com/paper/2203.04064/) (2022)
- [AUTOPILOT An Advanced Perception, Localization and Path Planning Techniques for Autonomous Vehicles Using YOLOv7 and MiDaS](https://mlsanity.com/paper/2610.06232/) (2026)

## Abstract (arXiv)

Over the past decade, many research articles have been published in the area of autonomous driving. However, most of them focus only on a specific technological area, such as visual environment perception, vehicle control, etc. Furthermore, due to fast advances in the self-driving car technology, such articles become obsolete very fast. In this paper, we give a brief but comprehensive overview on key ingredients of autonomous cars (ACs), including driving automation levels, AC sensors, AC software, open source datasets, industry leaders, AC applications and existing challenges.