# Safe Imitation Learning on Real-Life Highway Data for Human-like Autonomous Driving

arXiv: https://arxiv.org/abs/2110.04052 · HTML: https://mlsanity.com/paper/2110.04052/

## Key facts

- Cited by 5 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 7 of its 14 references that are in the map.
- First posted on arXiv 8 Oct 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, 7 of 7)

- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://mlsanity.com/paper/1812.03079/) (2018)
- [Multimodal End-to-End Autonomous Driving](https://mlsanity.com/paper/1906.03199/) (2019)
- [Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety](https://mlsanity.com/paper/1903.00640/) (2019)
- [Adversarial Driving: Attacking End-to-End Autonomous Driving](https://mlsanity.com/paper/2103.09151/) (2021)
- [PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving](https://mlsanity.com/paper/2011.00509/) (2020)
- [On the Sample Complexity of End-to-end Training vs. Semantic Abstraction Training](https://mlsanity.com/paper/1604.06915/) (2016)

## Built on by (most cited first, 5 of 5)

- [Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey](https://mlsanity.com/paper/2404.16356/) (2024)
- [Learning from Demonstrations of Critical Driving Behaviours Using Driver's Risk Field](https://mlsanity.com/paper/2210.01747/) (2022)
- [C-CoT: Counterfactual Chain-of-Thought with Vision-Language Models for Safe Autonomous Driving](https://mlsanity.com/paper/2605.10744/) (2026)
- [DeepIPCv2: LiDAR-powered Robust Environmental Perception and Navigational Control for Autonomous Vehicle](https://mlsanity.com/paper/2307.06647/) (2023)
- [Counter-example guided Imitation Learning of Feedback Controllers from Temporal Logic Specifications](https://mlsanity.com/paper/2403.16593/) (2024)

## Abstract (arXiv)

This paper presents a safe imitation learning approach for autonomous vehicle driving, with attention on real-life human driving data and experimental validation. In order to increase occupant's acceptance and gain drivers' trust, the autonomous driving function needs to provide a both safe and comfortable behavior such as risk-free and naturalistic driving. Our goal is to obtain such behavior via imitation learning of a planning policy from human driving data. In particular, we propose to incorporate barrier functions and smooth spline-based motion parametrization in the training loss function. The advantage is twofold: improving safety of the learning algorithm, while reducing the amount of needed training data. Moreover, the behavior is learned from highway driving data, which is collected consistently by a human driver and then processed towards a specific driving scenario. For development validation, a digital twin of the real test vehicle, sensors, and traffic scenarios are reconstructed toward high-fidelity and physics-based modeling technologies. These models are imported to simulation tools and co-simulated with the proposed algorithm for validation and further testing. Finally, we present experimental results and analyses, and compare with the conventional imitation learning technique (behavioral cloning) to justify the proposed development.