# Yaw-Guided Imitation Learning for Autonomous Driving in Urban Environments

arXiv: https://arxiv.org/abs/2111.06017 · HTML: https://mlsanity.com/paper/2111.06017/

## Key facts

- Cited by 1 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 17 of its 22 references that are in the map.
- First posted on arXiv 11 Nov 2021.

## 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, 17 of 17)

- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [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)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://mlsanity.com/paper/1812.03079/) (2018)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [Exploring the Limitations of Behavior Cloning for Autonomous Driving](https://mlsanity.com/paper/1904.08980/) (2019)
- [A Survey of End-to-End Driving: Architectures and Training Methods](https://mlsanity.com/paper/2003.06404/) (2020)
- [Multimodal End-to-End Autonomous Driving](https://mlsanity.com/paper/1906.03199/) (2019)
- [Urban Driving with Conditional Imitation Learning](https://mlsanity.com/paper/1912.00177/) (2019)
- [Grounding Human-to-Vehicle Advice for Self-driving Vehicles](https://mlsanity.com/paper/1911.06978/) (2019)
- [Conditional Affordance Learning for Driving in Urban Environments](https://mlsanity.com/paper/1806.06498/) (2018)
- [Multi-modal Sensor Fusion-Based Deep Neural Network for End-to-end Autonomous Driving with Scene Understanding](https://mlsanity.com/paper/2005.09202/) (2020)
- [End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners](https://mlsanity.com/paper/1803.10158/) (2018)
- [Learning Accurate and Human-Like Driving using Semantic Maps and Attention](https://mlsanity.com/paper/2007.07218/) (2020)

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

- [FastDriveVLA: Efficient End-to-End Driving via Plug-and-Play Reconstruction-based Token Pruning](https://mlsanity.com/paper/2507.23318/) (2025)

## Abstract (arXiv)

Existing imitation learning methods suffer from low efficiency and generalization ability when facing the road option problem in an urban environment. In this paper, we propose a yaw-guided imitation learning method to improve the road option performance in an end-to-end autonomous driving paradigm in terms of the efficiency of exploiting training samples and adaptability to changing environments. Specifically, the yaw information is provided by the trajectory of the navigation map. Our end-to-end architecture, Yaw-guided Imitation Learning with ResNet34 Attention (YILRatt), integrates the ResNet34 backbone and attention mechanism to obtain an accurate perception. It does not need high precision maps and realizes fully end-to-end autonomous driving given the yaw information provided by a consumer-level GPS receiver. By analyzing the attention heat maps, we can reveal some causal relationship between decision-making and scene perception, where, in particular, failure cases are caused by erroneous perception. We collect expert experience in the Carla 0.9.11 simulator and improve the benchmark CoRL2017 and NoCrash. Experimental results show that YILRatt has a 26.27% higher success rate than the SOTA CILRS. The code, dataset, benchmark and experimental results can be found at https://github.com/Yandong024/Yaw-guided-IL.git