# CARIL: Confidence-Aware Regression in Imitation Learning for Autonomous Driving

arXiv: https://arxiv.org/abs/2503.00783 · HTML: https://mlsanity.com/paper/2503.00783/

## 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 22 of its 36 references that are in the map.
- First posted on arXiv 2 Mar 2025.

## 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, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [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)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [Multi-Modal Fusion Transformer for End-to-End Autonomous Driving](https://mlsanity.com/paper/2104.09224/) (2021)
- [Exploring the Limitations of Behavior Cloning for Autonomous Driving](https://mlsanity.com/paper/1904.08980/) (2019)
- [Model-Based Imitation Learning for Urban Driving](https://mlsanity.com/paper/2210.07729/) (2022)
- [End-to-end Learning of Driving Models from Large-scale Video Datasets](https://mlsanity.com/paper/1612.01079/) (2016)
- [Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car](https://mlsanity.com/paper/1704.07911/) (2017)
- [Regression as Classification: Influence of Task Formulation on Neural Network Features](https://mlsanity.com/paper/2211.05641/) (2022)
- [End-to-End Deep Reinforcement Learning for Lane Keeping Assist](https://mlsanity.com/paper/1612.04340/) (2016)
- [Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments with Multimodal Sensor Fusion](https://mlsanity.com/paper/2005.01935/) (2020)
- [Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles](https://mlsanity.com/paper/2207.00788/) (2022)
- [Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models](https://mlsanity.com/paper/2409.16663/) (2024)
- [Uncertainty-Aware Data Aggregation for Deep Imitation Learning](https://mlsanity.com/paper/1905.02780/) (2019)
- [VTGNet: A Vision-based Trajectory Generation Network for Autonomous Vehicles in Urban Environments](https://mlsanity.com/paper/2004.12591/) (2020)
- [Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments](https://mlsanity.com/paper/2110.08586/) (2021)
- [PLM-Net: Perception Latency Mitigation Network for Vision-Based Lateral Control of Autonomous Vehicles](https://mlsanity.com/paper/2407.16740/) (2024)
- [NDST: Neural Driving Style Transfer for Human-Like Vision-Based Autonomous Driving](https://mlsanity.com/paper/2407.08073/) (2024)
- [End-to-End Steering for Autonomous Vehicles via Conditional Imitation Co-Learning](https://mlsanity.com/paper/2411.16131/) (2024)

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

- [ROAD: Responsibility-Oriented Reward Design for Reinforcement Learning in Autonomous Driving](https://mlsanity.com/paper/2505.24317/) (2025)

## Abstract (arXiv)

End-to-end vision-based imitation learning has demonstrated promising results in autonomous driving by learning control commands directly from expert demonstrations. However, traditional approaches rely on either regressionbased models, which provide precise control but lack confidence estimation, or classification-based models, which offer confidence scores but suffer from reduced precision due to discretization. This limitation makes it challenging to quantify the reliability of predicted actions and apply corrections when necessary. In this work, we introduce a dual-head neural network architecture that integrates both regression and classification heads to improve decision reliability in imitation learning. The regression head predicts continuous driving actions, while the classification head estimates confidence, enabling a correction mechanism that adjusts actions in low-confidence scenarios, enhancing driving stability. We evaluate our approach in a closed-loop setting within the CARLA simulator, demonstrating its ability to detect uncertain actions, estimate confidence, and apply real-time corrections. Experimental results show that our method reduces lane deviation and improves trajectory accuracy by up to 50%, outperforming conventional regression-only models. These findings highlight the potential of classification-guided confidence estimation in enhancing the robustness of vision-based imitation learning for autonomous driving. The source code is available at https://github.com/ElaheDlv/Confidence_Aware_IL.