# End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering

arXiv: https://arxiv.org/abs/2103.00345 · HTML: https://mlsanity.com/paper/2103.00345/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 44 references that are in the map.
- First posted on arXiv 27 Feb 2021.

## Builds on (most cited first, 26 of 26)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [Physical Adversarial Examples for Object Detectors](https://mlsanity.com/paper/1807.07769/) (2018)
- [ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector](https://mlsanity.com/paper/1804.05810/) (2018)
- [LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving](https://mlsanity.com/paper/2005.03778/) (2020)
- [A General Framework for Uncertainty Estimation in Deep Learning](https://mlsanity.com/paper/1907.06890/) (2019)
- [Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses](https://mlsanity.com/paper/1612.00197/) (2016)
- [SafetyNet: Detecting and Rejecting Adversarial Examples Robustly](https://mlsanity.com/paper/1704.00103/) (2017)
- [Deep Imitative Models for Flexible Inference, Planning, and Control](https://mlsanity.com/paper/1810.06544/) (2018)
- [Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack](https://mlsanity.com/paper/2009.06701/) (2020)
- [DARTS: Deceiving Autonomous Cars with Toxic Signs](https://mlsanity.com/paper/1802.06430/) (2018)
- [DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems](https://mlsanity.com/paper/1812.10812/) (2018)
- [Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems](https://mlsanity.com/paper/2001.10494/) (2020)
- [Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks](https://mlsanity.com/paper/1903.02193/) (2019)
- [LaneNet: Real-Time Lane Detection Networks for Autonomous Driving](https://mlsanity.com/paper/1807.01726/) (2018)
- [A Commute in Data: The comma2k19 Dataset](https://mlsanity.com/paper/1812.05752/) (2018)
- [Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning](https://mlsanity.com/paper/1809.02926/) (2018)
- [Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and Challenges](https://mlsanity.com/paper/1707.02432/) (2017)
- [Data Driven Prediction Architecture for Autonomous Driving and its Application on Apollo Platform](https://mlsanity.com/paper/2006.06715/) (2020)
- [Adaptive MPC for Autonomous Lane Keeping](https://mlsanity.com/paper/1806.04335/) (2018)

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

- [Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding](https://mlsanity.com/paper/2203.14141/) (2022)
- [Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction](https://mlsanity.com/paper/2205.14230/) (2022)
- [TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor](https://mlsanity.com/paper/2203.01261/) (2022)
- [Learning Representation for Anomaly Detection of Vehicle Trajectories](https://mlsanity.com/paper/2303.05000/) (2023)
- [Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling](https://mlsanity.com/paper/2309.09317/) (2023)

## Abstract (arXiv)

In the development of advanced driver-assistance systems (ADAS) and autonomous vehicles, machine learning techniques that are based on deep neural networks (DNNs) have been widely used for vehicle perception. These techniques offer significant improvement on average perception accuracy over traditional methods, however, have been shown to be susceptible to adversarial attacks, where small perturbations in the input may cause significant errors in the perception results and lead to system failure. Most prior works addressing such adversarial attacks focus only on the sensing and perception modules. In this work, we propose an end-to-end approach that addresses the impact of adversarial attacks throughout perception, planning, and control modules. In particular, we choose a target ADAS application, the automated lane centering system in OpenPilot, quantify the perception uncertainty under adversarial attacks, and design a robust planning and control module accordingly based on the uncertainty analysis. We evaluate our proposed approach using both the public dataset and production-grade autonomous driving simulator. The experiment results demonstrate that our approach can effectively mitigate the impact of adversarial attacks and can achieve 55% to 90% improvement over the original OpenPilot.