# An Overview about Emerging Technologies of Autonomous Driving

arXiv: https://arxiv.org/abs/2306.13302 · HTML: https://mlsanity.com/paper/2306.13302/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving.
- Builds on 32 of its 53 references that are in the map.
- First posted on arXiv 23 Jun 2023.

## Placed in

- End-to-end autonomous driving / [End-to-end system architecture](https://mlsanity.com/driving/end-to-end-system-architecture/)

## Builds on (most cited first, 30 of 32)

- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models](https://mlsanity.com/paper/2305.16986/) (2023)
- [EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought](https://mlsanity.com/paper/2305.15021/) (2023)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art](https://mlsanity.com/paper/1704.05519/) (2017)
- [Vision-Centric BEV Perception: A Survey](https://mlsanity.com/paper/2208.02797/) (2022)
- [Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe](https://mlsanity.com/paper/2209.05324/) (2022)
- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [A Survey of Deep Learning Applications to Autonomous Vehicle Control](https://mlsanity.com/paper/1912.10773/) (2019)
- [Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges](https://mlsanity.com/paper/2301.06262/) (2023)
- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [On the Design Fundamentals of Diffusion Models: A Survey](https://mlsanity.com/paper/2306.04542/) (2023)
- [Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses](https://mlsanity.com/paper/2104.01789/) (2021)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [A Survey on Simulators for Testing Self-Driving Cars](https://mlsanity.com/paper/2101.05337/) (2021)
- [BeyondPixels: A Comprehensive Review of the Evolution of Neural Radiance Fields](https://mlsanity.com/paper/2306.03000/) (2023)
- [Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2303.01212/) (2023)
- [MachMap: End-to-End Vectorized Solution for Compact HD-Map Construction](https://mlsanity.com/paper/2306.10301/) (2023)
- [High-Definition Map Generation Technologies For Autonomous Driving](https://mlsanity.com/paper/2206.05400/) (2022)
- [Bi-Mapper: Holistic BEV Semantic Mapping for Autonomous Driving](https://mlsanity.com/paper/2305.04205/) (2023)
- [Learning Accurate and Human-Like Driving using Semantic Maps and Attention](https://mlsanity.com/paper/2007.07218/) (2020)
- [A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-based Semantic Scene Understanding](https://mlsanity.com/paper/2209.05222/) (2022)
- [Occupancy Prediction-Guided Neural Planner for Autonomous Driving](https://mlsanity.com/paper/2305.03303/) (2023)
- [Cooperative Automated Vehicles: a Review of Opportunities and Challenges in Socially Intelligent Vehicles Beyond Networking](https://mlsanity.com/paper/1710.00461/) (2017)
- [Trajectory-Prediction with Vision: A Survey](https://mlsanity.com/paper/2303.13354/) (2023)
- [Energy-Based Models for Cross-Modal Localization using Convolutional Transformers](https://mlsanity.com/paper/2306.04021/) (2023)

## Built on by (most cited first, 2 of 2)

- [Applications of Large Scale Foundation Models for Autonomous Driving](https://mlsanity.com/paper/2311.12144/) (2023)
- [Beyond One Model Fits All: Ensemble Deep Learning for Autonomous Vehicles](https://mlsanity.com/paper/2312.05759/) (2023)

## Abstract (arXiv)

Since DARPA started Grand Challenges in 2004 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. This paper gives an overview about technical aspects of autonomous driving technologies and open problems. We investigate the major fields of self-driving systems, such as perception, mapping and localization, prediction, planning and control, simulation, V2X and safety etc. Especially we elaborate on all these issues in a framework of data closed loop, a popular platform to solve the long tailed autonomous driving problems.