# Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision

arXiv: https://arxiv.org/abs/1808.10393 · HTML: https://mlsanity.com/paper/1808.10393/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2018.

## Built on by (most cited first, 10 of 10)

- [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023)
- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [A Survey on Vision-Language-Action Models for Autonomous Driving](https://mlsanity.com/paper/2506.24044/) (2025)
- [Learning Interactive Driving Policies via Data-driven Simulation](https://mlsanity.com/paper/2111.12137/) (2021)
- [Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving](https://mlsanity.com/paper/2403.12176/) (2024)
- [Interpretable Decision-Making for End-to-End Autonomous Driving](https://mlsanity.com/paper/2508.18898/) (2025)
- [Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer Optimization](https://mlsanity.com/paper/1904.08784/) (2019)
- [SwapTransformer: highway overtaking tactical planner model via imitation learning on OSHA dataset](https://mlsanity.com/paper/2401.01425/) (2024)
- [Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation](https://mlsanity.com/paper/1909.09721/) (2019)
- [Optimality Inductive Biases and Agnostic Guidelines for Offline Reinforcement Learning](https://mlsanity.com/paper/2107.01407/) (2021)

## Abstract (arXiv)

Learning to drive faithfully in highly stochastic urban settings remains an open problem. To that end, we propose a Multi-task Learning from Demonstration (MT-LfD) framework which uses supervised auxiliary task prediction to guide the main task of predicting the driving commands. Our framework involves an end-to-end trainable network for imitating the expert demonstrator's driving commands. The network intermediately predicts visual affordances and action primitives through direct supervision which provide the aforementioned auxiliary supervised guidance. We demonstrate that such joint learning and supervised guidance facilitates hierarchical task decomposition, assisting the agent to learn faster, achieve better driving performance and increases transparency of the otherwise black-box end-to-end network. We run our experiments to validate the MT-LfD framework in CARLA, an open-source urban driving simulator. We introduce multiple non-player agents in CARLA and induce temporal noise in them for realistic stochasticity.