# Addressing Limitations of State-Aware Imitation Learning for Autonomous Driving

arXiv: https://arxiv.org/abs/2310.20650 · HTML: https://mlsanity.com/paper/2310.20650/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving.
- Builds on 36 of its 53 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Placed in

- End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/)
- Offline RL and imitation learning from data / [Imitation and offline RL for driving](https://mlsanity.com/offlinerl/imitation-and-offline-rl-for-driving/)

## Builds on (most cited first, 30 of 36)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2205.15997/) (2022)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning](https://mlsanity.com/paper/2207.07601/) (2022)
- [Exploring the Limitations of Behavior Cloning for Autonomous Driving](https://mlsanity.com/paper/1904.08980/) (2019)
- [Causal Confusion in Imitation Learning](https://mlsanity.com/paper/1905.11979/) (2019)
- [DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving](https://mlsanity.com/paper/1505.00256/) (2015)
- [End-to-End Urban Driving by Imitating a Reinforcement Learning Coach](https://mlsanity.com/paper/2108.08265/) (2021)
- [NEAT: Neural Attention Fields for End-to-End Autonomous Driving](https://mlsanity.com/paper/2109.04456/) (2021)
- [End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances](https://mlsanity.com/paper/1911.10868/) (2019)
- [Learning from All Vehicles](https://mlsanity.com/paper/2203.11934/) (2022)
- [Learning to drive from a world on rails](https://mlsanity.com/paper/2105.00636/) (2021)
- [Multimodal End-to-End Autonomous Driving](https://mlsanity.com/paper/1906.03199/) (2019)
- [CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving](https://mlsanity.com/paper/1807.03776/) (2018)
- [Explanations in Autonomous Driving: A Survey](https://mlsanity.com/paper/2103.05154/) (2021)
- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes](https://mlsanity.com/paper/1611.08323/) (2016)
- [Conditional Affordance Learning for Driving in Urban Environments](https://mlsanity.com/paper/1806.06498/) (2018)
- [On Offline Evaluation of Vision-based Driving Models](https://mlsanity.com/paper/1809.04843/) (2018)
- [GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving](https://mlsanity.com/paper/2111.08575/) (2021)
- [Multi-task Learning with Attention for End-to-end Autonomous Driving](https://mlsanity.com/paper/2104.10753/) (2021)
- [Context-Aware Synthesis and Placement of Object Instances](https://mlsanity.com/paper/1812.02350/) (2018)
- [A survey of robot learning from demonstrations for Human-Robot Collaboration](https://mlsanity.com/paper/1710.08789/) (2017)
- [End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception](https://mlsanity.com/paper/1801.06734/) (2018)
- [Explaining Autonomous Driving by Learning End-to-End Visual Attention](https://mlsanity.com/paper/2006.03347/) (2020)

## Built on by (most cited first, 3 of 3)

- [PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Driving](https://mlsanity.com/paper/2404.14327/) (2024)
- [PLM-Net: Perception Latency Mitigation Network for Vision-Based Lateral Control of Autonomous Vehicles](https://mlsanity.com/paper/2407.16740/) (2024)
- [Behavioral Cloning Models Reality Check for Autonomous Driving](https://mlsanity.com/paper/2409.07218/) (2024)

## Abstract (arXiv)

Conditional Imitation learning is a common and effective approach to train autonomous driving agents. However, two issues limit the full potential of this approach: (i) the inertia problem, a special case of causal confusion where the agent mistakenly correlates low speed with no acceleration, and (ii) low correlation between offline and online performance due to the accumulation of small errors that brings the agent in a previously unseen state. Both issues are critical for state-aware models, yet informing the driving agent of its internal state as well as the state of the environment is of crucial importance. In this paper we propose a multi-task learning agent based on a multi-stage vision transformer with state token propagation. We feed the state of the vehicle along with the representation of the environment as a special token of the transformer and propagate it throughout the network. This allows us to tackle the aforementioned issues from different angles: guiding the driving policy with learned stop/go information, performing data augmentation directly on the state of the vehicle and visually explaining the model's decisions. We report a drastic decrease in inertia and a high correlation between offline and online metrics.