# End-To-End Planning of Autonomous Driving in Industry and Academia: 2022-2023

arXiv: https://arxiv.org/abs/2401.08658 · HTML: https://mlsanity.com/paper/2401.08658/

## Key facts

- Cited by 11 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 10 of its 13 references that are in the map.
- First posted on arXiv 26 Dec 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, 10 of 10)

- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [Parting with Misconceptions about Learning-based Vehicle Motion Planning](https://mlsanity.com/paper/2306.07962/) (2023)
- [Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients](https://mlsanity.com/paper/2109.13333/) (2021)
- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving](https://mlsanity.com/paper/2310.05885/) (2023)
- [Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-like Autonomous Driving](https://mlsanity.com/paper/2212.08787/) (2022)
- [Tree-structured Policy Planning with Learned Behavior Models](https://mlsanity.com/paper/2301.11902/) (2023)
- [Imitation with Spatial-Temporal Heatmap: 2nd Place Solution for NuPlan Challenge](https://mlsanity.com/paper/2306.15700/) (2023)
- [Driving in Real Life with Inverse Reinforcement Learning](https://mlsanity.com/paper/2206.03004/) (2022)
- [Occupancy Prediction-Guided Neural Planner for Autonomous Driving](https://mlsanity.com/paper/2305.03303/) (2023)

## Built on by (most cited first, 11 of 11)

- [Exploring the Causality of End-to-End Autonomous Driving](https://mlsanity.com/paper/2407.06546/) (2024)
- [Astra: Toward General-Purpose Mobile Robots via Hierarchical Multimodal Learning](https://mlsanity.com/paper/2506.06205/) (2025)
- [Spatially Visual Perception for End-to-End Robotic Learning](https://mlsanity.com/paper/2411.17458/) (2024)
- [Automatic parking planning control method based on improved A* algorithm](https://mlsanity.com/paper/2406.15429/) (2024)
- [Automated Parking Planning with Vision-Based BEV Approach](https://mlsanity.com/paper/2406.15430/) (2024)
- [Acceleration method for generating perception failure scenarios based on editing Markov process](https://mlsanity.com/paper/2407.00980/) (2024)
- [Deep Reinforcement Learning for Adverse Garage Scenario Generation](https://mlsanity.com/paper/2407.01333/) (2024)
- [Research on Reliable and Safe Occupancy Grid Prediction in Underground Parking Lots](https://mlsanity.com/paper/2407.02197/) (2024)
- [Development of Occupancy Prediction Algorithm for Underground Parking Lots](https://mlsanity.com/paper/2409.00923/) (2024)
- [A Two-Stage Framework for Ego-Centric Key Object Identification via Object State Prediction](https://mlsanity.com/paper/2609.07125/) (2026)
- [A Comprehensive Review of Generative Physical Artificial Intelligence](https://mlsanity.com/paper/2609.18111/) (2026)

## Abstract (arXiv)

This paper aims to provide a quick review of the methods including the technologies in detail that are currently reported in industry and academia. Specifically, this paper reviews the end-to-end planning, including Tesla FSD V12, Momenta 2023, Horizon Robotics 2023, Motional RoboTaxi 2022, Woven Planet (Toyota): Urban Driver, and Nvidia. In addition, we review the state-of-the-art academic studies that investigate end-to-end planning of autonomous driving. This paper provides readers with a concise structure and fast learning of state-of-the-art end-to-end planning for 2022-2023. This article provides a meaningful overview as introductory material for beginners to follow the state-of-the-art end-to-end planning of autonomous driving in industry and academia, as well as supplementary material for advanced researchers.